Deploy Phase 2 dataset classifier (part 19)

Source GitHub commit: 740750973b9c67c0d55c209c006d860192f79dc2

.gitattributes

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+tests/fixtures/datasets/pdf/MERRA2.README.pdf filter=lfs diff=lfs merge=lfs -text
+tests/fixtures/datasets/pdf/README.ESMRN5L1.001.pdf filter=lfs diff=lfs merge=lfs -text
+tests/fixtures/datasets/pdf/README.OCO2.pdf filter=lfs diff=lfs merge=lfs -text
+tests/fixtures/datasets/pdf/README.OMPS_NPP_NMTO3_L2.2.pdf filter=lfs diff=lfs merge=lfs -text

outputs/small_run_gpt5.5_50/cache/0fdcb2c3d3fdd8eaac2a0eb8bcfcdd9d8acb515b3f16497a94fb6cbda73adf72.json

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+    "Abstract": "Cities and urban regions are responsible for significant amounts of greenhouse gas (GHG) emissions, which, along with associated air pollution byproducts, then dispersed locally globally. An investigation the complex dispersion dynamics is carried out downtown region Montreal, Canada, using computational fluid models. These models initialized validated measurements from a multiplatform field campaign day 21 February 2024. Measurements recorded atmospheric conditions aircraft flights radiosondes, Doppler lidar. Carbon dioxide (CO 2 ) methane (CH 4 concentrations were measured by aircraft, drone, Picarro GasScouter in fixed, location. This showed presence low-level jet, characteristics which investigated. The most suitable initialization determined, after thermal stability effects investigated comparing incompressible compressible numerical GHGs simulated considering emissions vehicles, McGill University's Ferrier building power plant, large government buildings. model predictions CO compare well rooftop measurements, while larger differences found CH comparisons drone profiles. findings suggest that traffic contribute significantly to local GHG point towards emission sources missing model. also used investigate across region, discussion their vertical distribution comparison against satellite measurements.",
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+    "Title": "Analysis of urban greenhouse gas dispersion for downtown Montreal using computational fluid dynamics and field measurements",
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+    "Abstract": "Abstract We present the first direct detection of fireworkgenerated infrasound in the upper mesosphere and lower thermosphere using a rocketborne sensor. A wideband capacitive differential pressure sensor (INF03D) was installed on board the MOMO3 sounding rocket, a privately developed vehicle launched from Taiki, Hokkaido, Japan, in May 2019, which reached a maximum altitude of 113 km. Ten spherical fireworks were launched from the ground before and after liftoff to provide temporally tagged acoustic sources. Here, denotes the rocket launch time ( s), and all time stamps are given relative to launch unless otherwise stated. Threedimensional raytracing simulations were performed with four atmospheric profile sets (NRLMSISE00/HWM14, JAWARA, ERA5, and MERRA2) to evaluate spatiotemporal intersections between acoustic ray paths and the rocket trajectory. Four distinct pressure transients with Hilbertenvelope amplitudes of 1.02.8 Pa were detected at 5076 km during ascent, falling within the modeled arrival windows for the T30 s and T90 s shots. In addition, two weak peaks (3.8 mPa each) were observed at 106109 km during descent, coinciding with a predicted intersection window and classified as marginal hits due to their limited signaltonoise ratios (SNR). Additional ascentphase transients around T+170T+185 s lacked corresponding rayrocket intersections, suggesting unmodeled propagation or nonfirework sources, and telemetry loss after T+282 s prevented evaluation of latedescent signals. These findings demonstrate that 0.12 Hz infrasound from nearsurface explosions can propagate to over 100 km altitude and that rocketborne sensors provide an effective platform for in situ acoustic measurements in the middle and upper atmosphere. , Plain Language Summary Sound from fireworks and other loud events can travel hundreds of kilometers upward into the lowdensity air near the edge of space. We installed a lightweight pressure sensor on a Japanese sounding rocket to directly listen for these very lowfrequency waves during a suborbital flight. During ascent between 45 and 109km altitude, the instrument recorded several brief pressure pulses. Using records of fireworks and a computer model of how sound travels through changing winds and temperatures, we found that multiple pulses lined up in both time and location with fireworks on the ground. The strongest signals had frequencies from 0.1 to 2 Hz, which can survive to high altitudes because lowfrequency waves are absorbed less by the atmosphere. These results show that rocketborne measurements can fill a gap between ground stations and balloons and can be used to test how the atmosphere guides infrasound from the surface to the lower thermosphere. , Key Points Rocketborne sensors detected infrasound between 45 km and the lower thermosphere, with a marginal signal near 109 km Ray tracing links multiple pressure peaks along the ascent to timestamped fireworks on the ground Lowfrequency (0.12 Hz) waves persist to high altitudes despite expected absorption",
+    "DOI": "10.1029/2025JD045676",
+    "Title": "First Detection of FireworkGenerated Infrasound in the Upper MesosphereLower Thermosphere With a RocketBorne Sensor: Results From the MOMO3 Sounding Rocket Experiment",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+        "classifier_evidence": "The study installed a rocket-borne pressure sensor and reports direct in situ acoustic/pressure measurements, including pressure transients and amplitudes at 50\u2013109 km altitude.",
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outputs/small_run_gpt5.5_50/cache/133de4fde86b86e64ad004717184e68ac8617a9849a612f5f533475193d6a5c0.json

@@ -0,0 +1,677 @@
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+    "Abstract": "Abstract Urban impacts on precipitation have been extensively studied in midlatitude developed countries. However, despite the recent rapid urban development, such studies focusing on Southeast Asian cities remain limited. This study evaluated urban impacts on precipitation in the Greater Ho Chi Minh City Metropolitan area (GHCM) by multicase numerical simulations using the Weather Research and Forecasting model (WRF) for 217 precipitation events during the rainy seasons from 2013 to 2022. We conducted each simulation with and without 2020 urban land use (URB and NO_URB), and under two different initial and boundary conditions to enhance the robustness of the results. Results show that daily precipitation over the central urban area was up to 30% greater in URB than in NO_URB, with statistical significance ( p < 0.05), especially between 15:00 and 22:00 LT. URB also exhibited higher sensible heat flux (+250 W m 2 ), higher convective boundary layer height (+350 m), and lower surface pressure (0.2 hPa) than NO_URB at 14:00 LT. These thermodynamic changes caused greater Convective Available Potential Energy and smaller Convective Inhibition than those of NO_URB, indicating intensified atmospheric instability due to urbanization. In addition, gridscale horizontal water vapor convergence was stronger in URB than in NO_URB. This convergence is caused by the lower surface pressure in URB than NO_URB. As a result, water vapor accumulated within the convective boundary layer. These results indicate that the urban area strengthened atmospheric instability and gridscale horizontal water vapor convergence, resulting in greater precipitation than the nonurban case. , Plain Language Summary Urban effects on rainfall have been well studied in developed countries in the midlatitudes. However, even though cities in Southeast Asia have grown rapidly in recent years, there are still only a few studies on this region. This study examined how urbanization affects rainfall in the Greater Ho Chi Minh City Metropolitan area (GHCM), using numerical simulations for 217 rainy events from 2013 to 2022. For each event, we ran two simulations: one that included the land use of urban areas as of 2020, and one that included urban areas removed. Our results showed that, in the urban scenario, daily rainfall in the central urban area was up to 30% greater than that in the nonurban scenario, especially between 15:00 and 22:00 LT. A key difference from cities in midlatitude regions is that this increase in rainfall lasted for a longer time in GHCM. We found two main reasons for the increased rainfall in the urban scenario. First, urbanization caused the atmosphere to become more unstable. Second, urbanization caused the horizontal moisture convergence in the urban area, and as a result, water vapor accumulated within the convective boundary layer. , Key Points Under synopticscale undisturbed conditions, rainfalls over the Greater Ho Chi Minh City Metropolitan Area increase because of urbanization Urbaninduced strong atmospheric instability and horizontal water vapor convergence were key drivers of the urbaninduced rainfall increase A set of 434 simulation cases enabled statistically robust assessment of urban impacts on precipitation",
+    "DOI": "10.1029/2025JD045001",
+    "Title": "Urban Impacts on Precipitation in the Greater Ho Chi Minh City Metropolitan Area, Vietnam",
+    "Year": 2026,
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+        "support_type": "explicit",
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+}

outputs/small_run_gpt5.5_50/cache/1417aae542fd8c058bb0cc592c48ac37b97cc7a065757bfa31aff2941b02794a.json

@@ -0,0 +1,455 @@
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+    "Abstract": "The Indo-Gangetic Plain (IGP) is a critical region for agricultural productivity, but its densely populated landscape faces significant challenges from aerosol pollution, largely driven by land use changes and agricultural practices. Understanding the spatiotemporal variation in Aerosol Optical Depth (AOD) is crucial for assessing its environmental and health impacts amid evolving agricultural practices. This study examines AOD trends across the upper, central, and lower IGP from 2001 to 2022, hypothesizing that changes in Land Use and Land Cover (LULC), particularly cropland expansion, along with associated agricultural practices of crop residue burning (CRB), are primary contributors. MODIS AOD and LULC datasets were used to assess seasonal trends alongside fire events and land use changes. Findings reveal significant AOD increases, particularly in the lower IGP, where vegetation loss and cropland expansion intensified CRB and aerosol emissions. Seasonal trends showed consistent AOD growth, with the lower IGP exhibiting the most pronounced increases in both AOD and fire counts. In contrast, the upper IGP exhibited reduced CRB activity and declining AOD trends during 2012 to 2022, indicating improved air quality. Analysis of Angstrom Exponent (AE) and Single Scattering Albedo (SSA) confirms the strong influence of biomass burning on AOD. Multivariate regression analysis further indicates that CRB is the dominant driver of AOD variability, while meteorological factors primarily modulate aerosol dispersion. These findings underscore the strong link between LULC changes, CRB, and AOD trends, highlighting the urgency of implementing sustainable agricultural practices and emission controls to mitigate pollution and enhance air quality across the IGP.",
+    "DOI": "10.1016/J.RSASE.2026.102055",
+    "Title": "Land use change-driven amplification of AOD over the Indo-Gangetic Plain",
+    "Year": 2026,
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+}

outputs/small_run_gpt5.5_50/cache/1adf5bf0a35d0b90f5d166002d1436409fd114ef8c5e5733a6dcdc2b535af725.json

@@ -0,0 +1,582 @@
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+    "DOI": "10.1029/2024MS004840",
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+    "Abstract": "Abstract As extreme precipitation events become more frequent and intense, localscale climate services are increasingly needed to help communities adapt. We here evaluate two fully coupled convectionpermitting Earth System Models for their ability to resolve mesoscale extreme weather events. Using the Integrated Forecasting System (IFS) and Icosahedral Nonhydrostatic Weather and Climate Model (ICON) within the Next Generation Earth Modeling Systems (nextGEMS) project, we evaluate their depiction of extreme precipitation with a focus on the Mediterranean region through a comparison with high resolution reanalysis, gridded observations, a regional climate model, and two lowerresolution climate models. The results are then compared at a common, coarser resolution globally. For dry extremes, we find that the higher resolution and hybrid/explicit representation of convection of the nextGEMS models improve the representation of dry day fraction over land by about 5%7% points. Generally, the nextGEMS models concentrate dry spells into limited frequency yet overly long periods, although the lack of convection parameterization in ICON reduces maximum annual dry spell length over land by 45 days compared to a lowerresolution model version. For wet extremes, the nextGEMS models properly high intensities of heavy precipitation, aside from overestimation in ICON over mountainous terrain. ICON, with no convection scheme, tends to create overly intense, small, convective cells that are triggered without moisture convergence. Overall, the depiction of wet and dry precipitation extremes in the Mediterranean region are representative of the nextGEMS' models performance across the global midlatitudes demonstrating the models' value in simulating extreme weather systems. , Plain Language Summary The latest computational advancements have led to the development of climate models that have high spatial resolutions and treat atmospheric convection in a way that resembles current weather forecasting models. These convectionpermitting atmosphereocean coupled models offer to complement largeensemble coarser global climate models and provide localscale climate information to help local communities adapt to changes in extreme weather caused by climate change. Here we examine the depiction of extreme precipitation events in two convectionpermitting Earth System Models developed in the Next Generation Earth Modeling Systems (nextGEMS) project. Over the Mediterranean region, these models are shown to improve the simulation of both dry and wet extremes including the frequency of dry hours and the occurrence of extreme rainfall. The lack of a convection scheme in ICON appears to improve most extreme precipitation metrics but also creates some new issues. Overall, the progress made in developing these models should allow for a better representation of drought and floods. The results found here appear applicable to other midlatitude regions globally marking these models as a step forward in future extreme weather prediction and adaptation. , Key Points Fully coupled convectionpermitting Earth System Models improve the depiction of wet/dry precipitation extremes over the Mediterranean region The explicit resolution of convection helps simulate accurate dry spells over land but creates overly intense convective precipitation The improvement of precipitation extrema in convectionpermitting Earth System Models is a key step toward local climate risk information",
+    "DOI": "10.1029/2024MS004840",
+    "Title": "Extreme Precipitation Depiction in ConvectionPermitting Earth System Models Within the nextGEMS Project",
+    "Year": 2025,
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+        "review_status": "not_required",
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outputs/small_run_gpt5.5_50/cache/1eb7791c864d9f0bb686bc4a27fe073aa52cb6fdef2f3acc2893768b6f0089f7.json

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+    "Abstract": "The literature on the relationship between extreme heat and mental health in low- middle-income countries has primarily focused long-term indirect channels, such as harvest failures their impacts health. In contrast, this study assesses direct, short-term effects of stress explores potential underlying physiological mechanisms. Moreover, by using a measure that accounts for non-linear interactions relative humidity (wet bulb temperature) comparing it with conventional temperature measures (dry temperature), I show neglecting interaction may underestimate true effect. combine data self-reported depression anxiety symptoms from four waves Indian WHO-SAGE survey high-resolution climate data, exploiting quasi-random variation exposure driven timing location implementation. results to increases reporting depressive symptoms, but not symptoms. are consistently smaller insignificant when dry is used together linear control variable. evidence points during agricultural work, increased cognitive difficulties, sleeping disturbances pathways linking among rural populations, while urban populations affected. Finally, shows access District Mental Health Program plays protective role, mitigating negative impact These underscore need integrate into assessments adaptation policies.",
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+        "term": "NATURAL HAZARDS",
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+        "term": "ATMOSPHERIC TEMPERATURE",
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+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+        "classifier_evidence": "Abstract compares wet bulb temperature with conventional dry temperature measures using high-resolution climate data to assess heat exposure; dry temperature corresponds to surface air temperature.",
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+}

outputs/small_run_gpt5.5_50/cache/22471946153b38cf8e59f0fb3e66bc92f5db4cf44c62d3da2ba433ab0f508f99.json

@@ -0,0 +1,208 @@
+{
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+  "identity": {
+    "DOI": "10.1016/J.NEXRES.2026.101707",
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+    "Abstract": " Carbon dioxide (CO\u2082) remains the principal greenhouse gas driving global warming. Rapid urbanization, intensified fossil fuel consumption, and the conversion of forested land to agriculture continue to accelerate CO\u2082 emissions, contributing to ocean acidification, extreme climate variability, and an increased frequency of environmental disasters. The column-averaged dry-air mole fraction of atmospheric carbon dioxide (XCO\u2082) serves as a vital metric for understanding the spatial and temporal dynamics of CO\u2082 concentrations in the Earth's atmosphere. In this study, we leverage observational data from two satellite missions, Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3), to generate high-resolution XCO\u2082 estimates at both regional and global scales. These platforms offer complementary coverage, yet their measurements are often hindered by cloud interference and limited temporal resolution, resulting in substantial data gaps. Traditional approaches for interpolating or reconstructing missing satellite observations frequently suffer from limited accuracy and scalability, especially in complex spatio-temporal contexts. To overcome these limitations, we propose a novel hybrid deep learning architecture that synergistically combines one-dimensional Convolutional Neural Networks (CNN1D) for local feature extraction with Long Short-Term Memory (LSTM) units to capture temporal dependencies across sequences. A key innovation in our framework is the integration of Monte Carlo Dropout (MCD) during inference. This technique introduces stochasticity into the prediction process, enabling the model to perform multiple forward passes and thereby quantify predictive uncertainty. By embedding MCD within the architecture, the model not only reconstructs missing XCO\u2082 values with high fidelity but also provides robust confidence estimates, an essential feature for downstream climate analysis and decision-making. Model performance was evaluated using quantitative metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and MSE-based training loss, benchmarked against ground-truth data from the Total Carbon Column Observing Network (TCCON). The CNN1D-LSTM-MCD model achieved superior predictive accuracy with a lowest average RMSE of 1.28 ppm, outperforming other known models in the literature. Uncertainty quantification via MCD revealed consistently low predictive uncertainty (<1.5 ppm) across evaluations. These findings validate the model\u2019s robustness, generalization capability, and reliability under limited data conditions. Visual assessments further confirmed the model\u2019s output consistency, spatial uniformity, and effective gap-filling performance. ",
+    "DOI": "10.1016/J.NEXRES.2026.101707",
+    "Title": "Robust Spatio-temporal reconstruction of XCO2 using OCO-2 and OCO-3 satellite observations and deep learning-based uncertainty quantification",
+    "Year": 2026,
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+        "classifier_evidence": "Title and abstract repeatedly identify CO\u2082/XCO\u2082, the column-averaged dry-air mole fraction of atmospheric carbon dioxide measured by OCO-2/OCO-3, as the study variable.",
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+          "valid": true,
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+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "ATMOSPHERIC CARBON DIOXIDE",
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+        "parent_uuid": "19ab681c-bdd7-4793-bbdb-1ec498575314",
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+          "CARBON AND HYDROCARBON COMPOUNDS",
+          "ATMOSPHERIC CARBON DIOXIDE"
+        ],
+        "reason_for_stopping": "The article focuses on XCO2, the column-averaged dry-air mole fraction of atmospheric carbon dioxide from OCO-2/OCO-3. This is not specifically a vertical carbon dioxide profile, surface CO2 measurement, or partial pressure of CO2, so the parent concept is the deepest supported among the supplied candidates.",
+        "review_required": false,
+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC CHEMISTRY",
+        "topic": "ATMOSPHERE",
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      {
+        "UUID": "5273c8c2-d30b-4666-b2d5-0388ce2741d0",
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+        "classifier_evidence": "The study reconstructs column-averaged atmospheric CO2 (XCO2) from OCO-2/OCO-3 satellite observations, describing spatial and temporal dynamics of CO2 concentrations in Earth's atmosphere.",
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+          "term": 0.93,
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+          "valid": true,
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+        "final_status": "accepted",
+        "level": "Term",
+        "name": "ATMOSPHERIC/OCEAN INDICATORS",
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+        "parent_uuid": "23703b6b-ee15-4512-b5b2-f441547e2edf",
+        "path_components": [
+          "CLIMATE INDICATORS",
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+        "reason_for_stopping": "The article\u2019s supported indicator is atmospheric column-averaged CO2 (XCO2) reconstruction from OCO-2/OCO-3 data, but no supplied direct-child candidate corresponds to atmospheric CO2/greenhouse gas concentration. Mentions of cloud interference and ocean acidification are contextual, not studied indicators.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC/OCEAN INDICATORS",
+        "topic": "CLIMATE INDICATORS",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+        "reason_for_stopping": "The article uses OCO-2/OCO-3 satellite XCO2 observations and discusses data gaps, cloud interference, and temporal coverage, which supports the broader SPECTRAL/ENGINEERING context, but it does not explicitly describe a supplied child term such as infrared wavelengths, hyperspectral/multispectral imagery, lidar, radar, or specific sensor/platform characteristics in enough detail for direct child assignment.",
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+        "review_status": "not_required",
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+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      "started_at": "2026-07-11T21:44:06.915454+00:00",
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+}

outputs/small_run_gpt5.5_50/cache/2362749bfc59a141b0636637d22cf663cd3aa3289d8dfeea2e349459868bc815.json

@@ -0,0 +1,370 @@
+{
+  "cache_key": "2362749bfc59a141b0636637d22cf663cd3aa3289d8dfeea2e349459868bc815",
+  "identity": {
+    "DOI": "10.3390/SU18094427",
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+  "result": {
+    "Abstract": "Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 237 rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROCAUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure.",
+    "DOI": "10.3390/SU18094427",
+    "Title": "Random Forest-Based Landslide Risk Assessment for Mountain Roads Under Extreme Rainfall: Implications for Infrastructure Resilience",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+      {
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+        "branch_id": "topic:topic_0011/term:term_0005",
+        "canonical_path": "SOLID EARTH > GEOMORPHIC LANDFORMS/PROCESSES",
+        "classifier_evidence": "The article focuses on rainfall-triggered landslides along mountain roads, including terrain controls, landslide inventories, and spatial risk assessment, which are geomorphic processes.",
+        "confidence": {
+          "final": 0.93,
+          "term": 0.93,
+          "topic": null,
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+}

outputs/small_run_gpt5.5_50/cache/297a90ddf7c0e444adc969bd5a4766b1f28ee71e861c7d5eb06216dd87b2a743.json

@@ -0,0 +1,385 @@
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+  "identity": {
+    "DOI": "10.1029/2025GL120807",
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+  },
+  "result": {
+    "Abstract": "Abstract Observations show a bimodal frequency distribution in total column vapor (TCV) over tropical oceans, with convective rainfall predominantly produced on the moist side of the frequency minimum between two modal peaks. Here we show a kmscale model of the tropics with explicit convection produces a bimodal TCV distribution, whereas the same model with parameterized convection does not. The parameterized model also fails to realistically confine rainfall to a moist mode. Using concepts from statistical mechanics we relate TCV frequency and tendency, and isolate process contributions to tendency in TCV phasespace. Where bimodality is lacking, we find an incorrect relationship between moisture flux convergence and TCV in environments with little or no rainfall. The resulting lack of a strong gradient in TCV tendency with respect to TCV is inconsistent with that expected to maintain a TCV frequency minimum. Our results demonstrate value in the TCV distribution as a process diagnostic. , Plain Language Summary It has been observed that the tropical atmosphere at any given time is mainly split into two regions: a moist region where columns contain high quantities of water vapor, and a drier region with less vapor. These regions are separated by a narrow margin zone with moderate amounts of moisture. In other words, moist and dry columns are both common, but marginal columns are less frequent. Models of the tropical atmosphere need to capture this distribution because moisture affects cloud and rainfall patterns, with implications for weather forecasting and for climate simulation. For a model of the entire tropical atmosphere on a kmscale grid, we show that a realistic column moisture distribution can be limited by model subgrid physics. Failure to realistically separate moist and dry regions of the distribution is associated with rainfall biases and with weaker redistribution of moisture by winds. , Key Points The tropical moisture distribution is bimodal in observations, but reproducing the bimodality in kmscale models depends on model physics The lack of bimodality is associated with excessive probability of light rainfall, specifically in dry to moderately moist columns In columns with low rain, the model without bimodality has a weak relationship between moisture and flux contributions to moisture tendency",
+    "DOI": "10.1029/2025GL120807",
+    "Title": "Tropical TCV as a Process Diagnostic: Connecting Probability to Processes in kmScale Models Via Moisture Budget Statistics",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
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+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR INDICATORS > TOTAL PRECIPITABLE WATER",
+        "classifier_evidence": "The title and abstract repeatedly identify total column vapor (TCV) / column moisture distribution over tropical oceans as the diagnostic variable, which corresponds to integrated column water vapor or total precipitable water.",
+        "confidence": {
+          "final": 0.92,
+          "term": null,
+          "topic": null,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "TOTAL PRECIPITABLE WATER",
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+        "path_components": [
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC WATER VAPOR",
+        "topic": "ATMOSPHERE",
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+      {
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+        "classifier_evidence": "Abstract explicitly identifies an incorrect relationship between moisture flux convergence and total column vapor, and key points discuss flux contributions to moisture tendency.",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WATER VAPOR",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "classifier_evidence": "Abstract states the study relates TCV frequency and tendency and isolates process contributions to tendency in TCV phase space; it also discusses gradients in TCV tendency with respect to TCV.",
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+        },
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+        },
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+        "final_status": "accepted",
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+        "path_components": [
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+          "WATER VAPOR PROCESSES",
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+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WATER VAPOR",
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+        "warnings": [
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+        "classifier_evidence": "Abstract explicitly states that \u201cconvective rainfall\u201d is predominantly produced on the moist side of the TCV distribution and compares models with explicit versus parameterized convection.",
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+          "ATMOSPHERE",
+          "PRECIPITATION",
+          "PRECIPITATION RATE",
+          "CONVECTIVE PRECIPITATION"
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+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "PRECIPITATION",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "f29968d9-b911-45b5-b5b5-0a759a345ce9",
+        "branch_id": "topic:topic_0002/term:term_0014/variable:variable_0011/variable:variable_0003",
+        "canonical_path": "ATMOSPHERE > PRECIPITATION > PRECIPITATION RATE > SURFACE PRECIPITATION",
+        "classifier_evidence": "The abstract repeatedly discusses rainfall occurrence, light rainfall, rainfall biases, and how rainfall relates to column moisture in tropical model simulations.",
+        "confidence": {
+          "final": 0.65,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": 0.65,
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+        },
+        "deterministic_validation": {
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+          "valid": true,
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+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "SURFACE PRECIPITATION",
+        "original_candidate": null,
+        "parent_uuid": "ac50c468-df2f-429c-8394-9d63efcc6f9d",
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+          "ATMOSPHERE",
+          "PRECIPITATION",
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+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "PRECIPITATION",
+        "topic": "ATMOSPHERE",
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+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          },
+          {
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+              "level": "Variable_Level_2",
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+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+          }
+        ]
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+      {
+        "UUID": "df160e31-ae45-41a4-9093-a80fe5303cea",
+        "branch_id": "topic:topic_0002/term:term_0011",
+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC WINDS",
+        "classifier_evidence": "The abstract analyzes moisture flux convergence and notes weaker redistribution of moisture by winds.",
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+        "final_status": "accepted",
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+        "name": "ATMOSPHERIC WINDS",
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+        "parent_uuid": "c47f6052-634e-40ef-a5ac-13f69f6f4c2a",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC WINDS"
+        ],
+        "reason_for_stopping": "The article only refers generally to winds through moisture redistribution and moisture flux convergence in tropical moisture budgets. It does not provide evidence for a specific wind subtype such as surface, upper-level, local winds, wind profiles, indices, or measurements of wind dynamics.",
+        "review_required": false,
+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WINDS",
+        "topic": "ATMOSPHERE",
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+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
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+    },
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+    "review_status": "not_required",
+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/30a5526d97f544310950fe44cc50106008adf3bd24dccbffff3d55015bc84c87.json

@@ -0,0 +1,273 @@
+{
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+  "identity": {
+    "DOI": "10.1029/2025JD045573",
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+    "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
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+  "result": {
+    "Abstract": "Abstract Atmospheric rivers (ARs) were first documented by Zhu and Newell for transporting global water vapor. ARs contribute to extreme rainfall, especially over the Western United States. The primary water vapor source of ARs is from the tropical ocean, where convective systems bring the moist flux upward from the surface to the troposphere. Previous studies have investigated ARs in connection to the MaddenJulian Oscillation (MJO) using the Realtime Multivariate MJO (RMM) Index, which is based on Empirical Orthogonal Function (EOF) analysis of outgoing longwave radiation and upperlevel wind. The question of what is the physical mechanism connecting the MJO and ARs remains unclear. This study aims to provide new insights into the effect of MJO convection as a water vapor source for ARs by investigating the direct connection between the MJO convection and ARs using the Largescale Precipitation Tracking (LPT) developed by Kerns and Chen. We track the MJO largescale precipitation and ARs in time and space using satellite data and reanalysis data from 2000 to 2024. We find that largescale convection of the MJO serves as a major water vapor source for ARs during the boreal winter (DecemberMarch) when the MJO LPT systems extended further in the westcentral Pacific. During these months, ARs are twice as likely to occur when the MJO convection is active. ARs are stronger when they are physically connected to MJO convection. These stronger ARs are more likely to lead to increased extreme rainfall and flood risk along the U.S. West Coast. , Plain Language Summary Atmospheric rivers (ARs), vehicles of water vapor transport from the tropics to the midlatitudes, play a key role in extreme rainfall and flooding on the West Coast of the United States. Past research has linked the ARs to the MaddenJulian Oscillation (MJO) using common statistical indices, but these indices cannot capture the actual physical connection between the two, especially the intensity and duration of ARs to the MJO convection. To better understand the real connection of the two physical systems, we use a tracking method that follows the MJO largescale precipitation in connection to ARs from 2000 through 2024. We find that explicitly tracking the location of the MJO precipitation for individual MJO events provides new insights on the role of the MJO, as the main water source, in AR intensity and duration. In particular, we find that ARs that physically overlap with MJO rainfall last longer, are larger, and are more intense. These ARs are also more likely to lead to flooding events in California, Oregon, and Washington during the boreal winter (DecemberMarch). , Key Points Largescale convection linked to the MaddenJulian Oscillation (MJO) is a major water vapor source for wintertime atmospheric rivers (ARs) ARs are about twice as likely to occur when MJO convection is active. They tend to be stronger when physically connected to MJO convection These stronger ARs, associated with MJO convection, are linked to extreme rainfall and heightened flood risk along the U.S. West Coast",
+    "DOI": "10.1029/2025JD045573",
+    "Title": "MaddenJulian Oscillation and Atmospheric Rivers: New Insights on Water Source and Transport for Extreme Rainfall Over the Western U.S.",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
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+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROCESSES > WATER VAPOR FLUX",
+        "classifier_evidence": "The article repeatedly describes atmospheric rivers as transporting water vapor and refers to moist flux from the tropical ocean/surface to the troposphere as the source and transport mechanism for ARs.",
+        "confidence": {
+          "final": 0.92,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": 0.92,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "WATER VAPOR FLUX",
+        "original_candidate": null,
+        "parent_uuid": "3c4fe00c-6fb1-403e-a053-3a0174a6dfe6",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC WATER VAPOR",
+          "WATER VAPOR PROCESSES",
+          "WATER VAPOR FLUX"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WATER VAPOR",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0008"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "09a57dc7-3911-4a65-9f12-b819652b8671",
+        "branch_id": "topic:topic_0002/term:term_0014/variable:variable_0005/variable:variable_0003",
+        "canonical_path": "ATMOSPHERE > PRECIPITATION > LIQUID PRECIPITATION > RAIN",
+        "classifier_evidence": "The article repeatedly discusses extreme rainfall over the Western U.S., MJO rainfall/precipitation, and ARs leading to increased rainfall and flooding.",
+        "confidence": {
+          "final": 0.95,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": 0.95,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "RAIN",
+        "original_candidate": null,
+        "parent_uuid": "7d45f108-dda2-4341-b853-ee3a490aad59",
+        "path_components": [
+          "ATMOSPHERE",
+          "PRECIPITATION",
+          "LIQUID PRECIPITATION",
+          "RAIN"
+        ],
+        "reason_for_stopping": "The article discusses extreme rainfall associated with atmospheric rivers and MJO convection, but provides no evidence for acid rain or freezing rain specifically.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "PRECIPITATION",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0003"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "cad5c02a-e771-434e-bef6-8dced38a68e8",
+        "branch_id": "topic:topic_0002/term:term_0014/variable:variable_0007",
+        "canonical_path": "ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT",
+        "classifier_evidence": "The abstract describes increased extreme rainfall and flooding risk along the U.S. West Coast associated with stronger atmospheric rivers.",
+        "confidence": {
+          "final": 0.68,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.68,
+          "variable_level_2": null,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "PRECIPITATION AMOUNT",
+        "original_candidate": null,
+        "parent_uuid": "1532e590-a62d-46e3-8d03-2351bc48166a",
+        "path_components": [
+          "ATMOSPHERE",
+          "PRECIPITATION",
+          "PRECIPITATION AMOUNT"
+        ],
+        "reason_for_stopping": "The article discusses precipitation/rainfall amount and tracking of MJO large-scale precipitation, but does not specify a 12-hour, 24-hour, 3/6-hour, or hourly precipitation accumulation period.",
+        "review_required": true,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "inferred",
+        "term": "PRECIPITATION",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0007"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          },
+          {
+            "DOI": null,
+            "code": "REVIEW_RECOMMENDED_WEAK_SUPPORT",
+            "details": {
+              "confidence_final": 0.68,
+              "level": "Variable_Level_1",
+              "support_type": "inferred"
+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
+            "stage": "review_flagging"
+          }
+        ]
+      },
+      {
+        "UUID": "f6b314db-883a-4493-9140-b6afda949710",
+        "branch_id": "topic:topic_0002/term:term_0016/variable:variable_0012",
+        "canonical_path": "ATMOSPHERE > WEATHER EVENTS > RAIN STORMS",
+        "classifier_evidence": "The abstract repeatedly links atmospheric rivers to \u201cextreme rainfall\u201d over the Western United States and states stronger ARs are more likely to lead to increased extreme rainfall along the U.S. West Coast.",
+        "confidence": {
+          "final": 0.82,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.82,
+          "variable_level_2": null,
+          "variable_level_3": null
+        },
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+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "RAIN STORMS",
+        "original_candidate": null,
+        "parent_uuid": "b7d562cf-9b9b-4461-900b-50423a8c4d29",
+        "path_components": [
+          "ATMOSPHERE",
+          "WEATHER EVENTS",
+          "RAIN STORMS"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "mixed",
+        "term": "WEATHER EVENTS",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      }
+    ],
+    "errors": [],
+    "no_classification_reason": null,
+    "processing_metadata": {
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+      "article_fingerprint": "ce0f83f2ca0429caf8decfb61e4c5d0548971bf06ad67aef3c11a2016974cb83",
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+      "completed_at": "2026-07-11T20:57:40.134684+00:00",
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+        "topic": "topic-v1",
+        "variable": "variable-v1"
+      },
+      "run_id": "manual-small-run",
+      "started_at": "2026-07-11T20:56:49.992041+00:00",
+      "title_available": true,
+      "vocabulary_hash": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad",
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+    },
+    "processing_status": "completed",
+    "review_status": "not_required",
+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/32dafcda8282277c6d117b40778bad91fc2814eba00c97a76aa36f2a16de7c5e.json

@@ -0,0 +1,210 @@
+{
+  "cache_key": "32dafcda8282277c6d117b40778bad91fc2814eba00c97a76aa36f2a16de7c5e",
+  "identity": {
+    "DOI": "10.1016/J.ATMOSRES.2026.108965",
+    "application_version": "0.1.0",
+    "article_fingerprint": "d8085f69bca30d2dbc9fd171d65d6322a852b870fb378c2374ff33a7e981738d",
+    "configuration_hash": "92d5193323468144f8da5f7c8629eb8dca34a0b1a1b0d4b74b09ee2b478c90fc",
+    "model_max_retries": 2,
+    "model_name": "gpt-5.5",
+    "model_provider": "openai",
+    "model_temperature": 0.0,
+    "model_timeout_seconds": 30.0,
+    "prompt_version_term": "term-v1",
+    "prompt_version_topic": "topic-v1",
+    "prompt_version_variable": "variable-v1",
+    "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
+  },
+  "result": {
+    "Abstract": "This study presents a continental-scale hail climatology for Europe (20142024) using the Global Precipitation Measurement (GPM) Dual-frequency Radar (DPR). To address limitations of inhomogeneous surface observations, four detection algorithms were applied at radar-pixel level. Normalized frequency maps (01) generated AprilSeptember period, individual months, and subregions, to identify analyze European hotspots. The results main epicenter near Alpine range, with secondary axes extending over Balkan peninsula Apennines, confirming orography as dominant influence. analysis revealed clear seasonal evolution. That is, activity began in April scattered foci, shifted toward southeastern May, reached its maximum spatial extent heterogeneity June, consolidated system during summer peak (JulyAugust), finally moved Mediterranean Sea September. derived robustly validates documented patterns, provides new continental details, addresses observational gaps terrestrial networks, thereby value satellite radar studying extreme events. A is from GPM-DPR sensor data. Alps are control. Secondary high-frequency identified Balkans Apennines. Hail shifts southeast May",
+    "DOI": "10.1016/J.ATMOSRES.2026.108965",
+    "Title": "A climatology of hail in Europe (20142024) based on GPM-DPR sensor data",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
+        "UUID": "7118d286-6629-48e5-931f-052cd347395e",
+        "branch_id": "topic:topic_0002/term:term_0014/variable:variable_0013/variable:variable_0002",
+        "canonical_path": "ATMOSPHERE > PRECIPITATION > SOLID PRECIPITATION > HAIL",
+        "classifier_evidence": "Title states a climatology of hail in Europe; abstract repeatedly describes hail climatology and hail activity using GPM-DPR sensor data.",
+        "confidence": {
+          "final": 0.99,
+          "term": null,
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+          "variable_level_1": null,
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+}

outputs/small_run_gpt5.5_50/cache/35b1116b8453d820d3bf863d8db5d011a9bdeba9de49262af8bc72330508a6e1.json

@@ -0,0 +1,1322 @@
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+  "identity": {
+    "DOI": "10.1029/2024RG000874",
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+  "result": {
+    "Abstract": "Abstract Sea ice is a crucial component of polar climate systems and is undergoing substantial changes in both hemispheres due to evolving climatic conditions. Arctic sea ice is transitioning from perennial to seasonal cover, and the Southern Ocean sea ice is exhibiting recent minima and enhanced seasonality. As global warming continues, the role of sea ice in polar climate systems is expected to transform further. However, many theoretical frameworks and parameterizations in current seaice models are based on observations from an earlier era dominated by thicker multiyear ice. Here, we synthesize the physical processes governing the dynamics and thermodynamics of drift icethe mobile pack iceand its coupling with the atmospheric and oceanic boundary layers. Our goal is to provide a coherent theoretical framework of the seaice evolution equations and to summarize parameterizations of subgrid processes used across models of varying complexity. These include representations of momentum and scalar fluxes, icethickness distribution and redistribution, snow and meltpond processes, waveice interactions, and physicalbiogeochemical feedbacks. We also examine how sea ice impacts ocean stratification and mixing, as well as the atmospheric boundary layer and clouds. Finally, we highlight recent observational findings and outline priorities for improving the representation of driftice processes, particularly in light of the changing climate and ice state. , Plain Language Summary Polar regions are warming faster than the rest of the planet. This has caused substantial seaice loss and changes in the physical properties of the drifting seaice cover. As these trends continue, it is critical to understand the processes that govern the evolving seaice conditions, how their relative importance may shift, and how they are represented in models. This article reviews how drift icethe mobile, floating packforms, melts, moves, and interacts with the ocean below and the atmosphere above. We bring together the key ideas and equations behind these processes and explain how they are typically included in models (parameterizations). Because many commonly used formulations were developed from observations during an era when the ice was thicker and older, we highlight the processes most affected by the shift toward thinner, more seasonal ice in both hemispheres. Finally, we outline key uncertainties and priorities for improvement. , Key Points This review provides an uptodate theoretical framework of oceanseaiceatmosphere interactions We summarize governing equations and parametrizations commonly used in seaice models and propose improvements based on recent observations Focus is on processes affected by the shift in seaice characteristics from multiyear ice to younger, thinner, and more dynamic ice types",
+    "DOI": "10.1029/2024RG000874",
+    "Title": "Processes Driving DriftIce Evolution and Its Interaction With the Oceanic and Atmospheric Boundary Layers",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
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+        "branch_id": "topic:topic_0006/term:term_0004/variable:variable_0018",
+        "canonical_path": "CRYOSPHERE > SEA ICE > PACK ICE",
+        "classifier_evidence": "The abstract defines the focus as drift ice, described as \"the mobile pack ice.\"",
+        "confidence": {
+          "final": 0.95,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.95,
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+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "PACK ICE",
+        "original_candidate": null,
+        "parent_uuid": "860e2af9-ce29-4f3f-b027-ae3747eb3e01",
+        "path_components": [
+          "CRYOSPHERE",
+          "SEA ICE",
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+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "SEA ICE",
+        "topic": "CRYOSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0018"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
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+        "classifier_evidence": "The article synthesizes processes governing sea-ice \"dynamics and thermodynamics\" and interactions with oceanic and atmospheric boundary layers.",
+        "confidence": {
+          "final": 0.95,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.95,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
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+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "SEA ICE DYNAMICS",
+        "original_candidate": null,
+        "parent_uuid": "860e2af9-ce29-4f3f-b027-ae3747eb3e01",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "SEA ICE",
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+            },
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0006/term:term_0004/variable:variable_0027",
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+        "classifier_evidence": "The plain-language summary says the review covers how drifting sea ice \"moves\" and the abstract emphasizes mobile drift ice and momentum fluxes.",
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+        },
+        "deterministic_validation": {
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+          "valid": true,
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+        },
+        "errors": [],
+        "final_status": "accepted",
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+        "name": "SEA ICE MOTION",
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+        "parent_uuid": "860e2af9-ce29-4f3f-b027-ae3747eb3e01",
+        "path_components": [
+          "CRYOSPHERE",
+          "SEA ICE",
+          "SEA ICE MOTION"
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "SEA ICE",
+        "topic": "CRYOSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0027"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0006/term:term_0004/variable:variable_0010",
+        "canonical_path": "CRYOSPHERE > SEA ICE > ICE GROWTH/MELT",
+        "classifier_evidence": "The summary states the review explains how drift ice \"forms, melts\" and covers thermodynamic evolution.",
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+          "variable_level_1": 0.9,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "ICE GROWTH/MELT",
+        "original_candidate": null,
+        "parent_uuid": "860e2af9-ce29-4f3f-b027-ae3747eb3e01",
+        "path_components": [
+          "CRYOSPHERE",
+          "SEA ICE",
+          "ICE GROWTH/MELT"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "SEA ICE",
+        "topic": "CRYOSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0010"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0006/term:term_0004/variable:variable_0005",
+        "canonical_path": "CRYOSPHERE > SEA ICE > ICE DEPTH/THICKNESS",
+        "classifier_evidence": "The abstract discusses thicker multiyear ice, thinner seasonal ice, and \"ice thickness distribution and redistribution.\"",
+        "confidence": {
+          "final": 0.88,
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+          "variable_level_1": 0.88,
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+        "final_status": "accepted",
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+        "name": "ICE DEPTH/THICKNESS",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
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+        ]
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+          "final": 0.86,
+          "term": null,
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+          "variable_level_1": 0.86,
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+        },
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+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "MELT POND",
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+        "parent_uuid": "860e2af9-ce29-4f3f-b027-ae3747eb3e01",
+        "path_components": [
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "SEA ICE",
+        "topic": "CRYOSPHERE",
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0006/term:term_0004/variable:variable_0013",
+        "canonical_path": "CRYOSPHERE > SEA ICE > ICE TYPES",
+        "classifier_evidence": "Key points emphasize shifts from multiyear ice to \"younger, thinner, and more dynamic ice types.\"",
+        "confidence": {
+          "final": 0.82,
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+          "topic": null,
+          "variable_level_1": 0.82,
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+        },
+        "deterministic_validation": {
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+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "ICE TYPES",
+        "original_candidate": null,
+        "parent_uuid": "860e2af9-ce29-4f3f-b027-ae3747eb3e01",
+        "path_components": [
+          "CRYOSPHERE",
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+          "ICE TYPES"
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+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "SEA ICE",
+        "topic": "CRYOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0006/term:term_0004/variable:variable_0022",
+        "canonical_path": "CRYOSPHERE > SEA ICE > SEA ICE AGE",
+        "classifier_evidence": "The abstract contrasts perennial, seasonal, multiyear, younger, and thinner ice in changing polar conditions.",
+        "confidence": {
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+          "variable_level_1": 0.82,
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+        "deterministic_validation": {
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+        "errors": [],
+        "final_status": "accepted",
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outputs/small_run_gpt5.5_50/cache/382905aa09e6b97e2f28fba39ab7199947ede16d34c671c3f0d940e1001dc568.json

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+}

outputs/small_run_gpt5.5_50/cache/39f66515b7469ca1a460fca8b0d3f6d48cbe6d97d2b7dae9f3255c86049e70ea.json

@@ -0,0 +1,848 @@
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+          {
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+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+        "classifier_evidence": "Abstract relates rainfall trends and initiation conditions to Metro Manila urban expansion and local surface variables such as temperature and evaporation.",
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+        "review_required": true,
+        "review_status": "not_required",
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+        "topic": "CLIMATE INDICATORS",
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+}

outputs/small_run_gpt5.5_50/cache/3d0540e96204847b4e0275724da883ffb4c442dc095392be98dee305fad04e39.json

@@ -0,0 +1,209 @@
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+    "DOI": "10.1016/J.ASTROPARTPHYS.2026.103252",
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+  "result": {
+    "Abstract": "We study the influence of variations in the upper atmospheric temperature and interplanetary magnetic field on the cosmic ray induced atmospheric muon flux measured by the GRAPES-3 experiment over 22 years (20012022) of data; spanning three solar cycles: the declining phase of Solar Cycle 23, the full Cycle 24, and the rising and maximum phases of Cycle 25. Located in Ooty, India, the GRAPES-3 large area (560m2) muon telescope detects 4 billion muons daily above 1GeV, with an angular resolution of 4, enabling a statistical precision <0.01% on the hourly muon rate. After accounting for the effect of atmospheric pressure variations, we compare this data with the upper atmospheric temperature inferred from NASA's MERRA-2 dataset as well as magnetic field data from the ACE and WIND spacecraft at Lagrange point L1. A simultaneous iterative fitting method employing Fast Fourier Transforms and a narrow band-pass filter reveals the temperature and magnetic field coefficients to be T=0.22410.04(stat.)0.0220(syst.)%K1 and M=0.5740.027(stat.)0.011(syst.)%nT1, respectively, for an assumed hadronic attenuation length =120 g cm2, underscoring the potential of the GRAPES-3 muon telescope to serve as a real time monitor of the upper atmospheric temperature or interplanetary magnetic field.",
+    "DOI": "10.1016/J.ASTROPARTPHYS.2026.103252",
+    "Title": "Monitoring the upper atmospheric temperature and interplanetary magnetic field with the GRAPES-3 muon telescope",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+      {
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+        "canonical_path": "SUN-EARTH INTERACTIONS > SOLAR ACTIVITY > COSMIC RAYS",
+        "classifier_evidence": "The abstract explicitly studies \"cosmic ray induced atmospheric muon flux\" measured by GRAPES-3 over multiple solar cycles and its relation to interplanetary magnetic field variations.",
+        "confidence": {
+          "final": 0.9,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.9,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "COSMIC RAYS",
+        "original_candidate": null,
+        "parent_uuid": "2e83362e-d8f8-4bca-83fd-bae360ebe94b",
+        "path_components": [
+          "SUN-EARTH INTERACTIONS",
+          "SOLAR ACTIVITY",
+          "COSMIC RAYS"
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+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "SOLAR ACTIVITY",
+        "topic": "SUN-EARTH INTERACTIONS",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0004"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "926c1b80-6c11-40eb-ae7f-f5bcfdc43fac",
+        "branch_id": "topic:topic_0002/term:term_0009/variable:variable_0004",
+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC TEMPERATURE > UPPER AIR TEMPERATURE",
+        "classifier_evidence": "Title and abstract repeatedly state monitoring/studying \u201cupper atmospheric temperature,\u201d comparing muon data with upper atmospheric temperature inferred from NASA MERRA-2.",
+        "confidence": {
+          "final": 0.98,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.98,
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+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "UPPER AIR TEMPERATURE",
+        "original_candidate": null,
+        "parent_uuid": "35e1f93b-99b3-4430-b477-0ecafa80d67a",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC TEMPERATURE",
+          "UPPER AIR TEMPERATURE"
+        ],
+        "reason_for_stopping": "The article supports upper atmospheric temperature broadly, using MERRA-2 temperature data and studying temporal variations, but does not specifically identify temperature anomalies, vertical profiles, dew point, virtual, deiced, or boundary-layer temperature.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC TEMPERATURE",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0004"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "08fd82a1-4370-46a2-82ea-94c0f91498a7",
+        "branch_id": "topic:topic_0002/term:term_0007",
+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC PRESSURE",
+        "classifier_evidence": "Abstract states the analysis accounts for the effect of atmospheric pressure variations on the measured muon rate.",
+        "confidence": {
+          "final": 0.72,
+          "term": 0.72,
+          "topic": null,
+          "variable_level_1": null,
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+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Term",
+        "name": "ATMOSPHERIC PRESSURE",
+        "original_candidate": null,
+        "parent_uuid": "c47f6052-634e-40ef-a5ac-13f69f6f4c2a",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC PRESSURE"
+        ],
+        "reason_for_stopping": "The abstract only states that atmospheric pressure variations were accounted for, without specifying a supported child concept such as a pressure measurement type, anomaly, surface pressure, sea level pressure, or pressure tendency.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC PRESSURE",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      }
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+      "started_at": "2026-07-11T21:07:43.585115+00:00",
+      "title_available": true,
+      "vocabulary_hash": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad",
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+    },
+    "processing_status": "completed",
+    "review_status": "not_required",
+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/3db6ce00c22439b787dffc7de82d32d534d4694bfa02217b760a68722115ccff.json

@@ -0,0 +1,225 @@
+{
+  "cache_key": "3db6ce00c22439b787dffc7de82d32d534d4694bfa02217b760a68722115ccff",
+  "identity": {
+    "DOI": "10.1029/2025WR040312",
+    "application_version": "0.1.0",
+    "article_fingerprint": "04f5ff43bd5a578999aa70bdf927baca027a1b4d644f036d23bac0dbb237d458",
+    "configuration_hash": "92d5193323468144f8da5f7c8629eb8dca34a0b1a1b0d4b74b09ee2b478c90fc",
+    "model_max_retries": 2,
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+    "model_temperature": 0.0,
+    "model_timeout_seconds": 30.0,
+    "prompt_version_term": "term-v1",
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+    "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
+  },
+  "result": {
+    "Abstract": "Abstract Knowledge of the groundwater recharge rate determines whether aquifer use is sustainable. However, accurately measuring recharge globally presents significant challenges due to the complexity of subsurface processes and the lack of direct observational methods. This study addresses these challenges by developing a methodology that integrates satellite data, numerical models, and machine learning to estimate groundwater recharge globally. The methodology involves two steps. First, we run a numerical model, Hydrus1D, to simulate soil moisture fluxes in the unsaturated zone by solving the Richards equation in the vertical direction for 235 different points representing various climates and soil types across the globe. Second, using Hydrus1D inputs and outputs, we train a supervised ensemble machinelearning model, specifically a Gaussian Process Regression model, as an emulator to mimic Hydrus1D. This enables us to process satellite observations efficiently to estimate annual recharge flux globally. Inputs for the model include NASA's SMAP soil moisture and GPM precipitation observations, ERA5 climate reanalysis data, and soil hydraulic properties. Rainfall, unsaturated hydraulic conductivity, and soil moisture are identified as the most significant predictors of groundwater recharge. The approach effectively captures global recharge patterns, particularly in regions with high rainfall, though it shows some limitations in arid areas with minimal recharge and heavily irrigated areas. We confirm the reasonableness of recharge estimates by comparing them with observed changes in subsurface water storage from the GRACE satellite mission. The method effectively captures the observed trends in water storage, demonstrating the model's capability to estimate recharge using largescale satellite and reanalysis data. , Plain Language Summary We need to know how much groundwater is replenished to ensure its sustainable use, but measuring the recharge that replenishes groundwater globally is difficult, if not impossible. This study develops a new method that combines satellite data, computer models, and machine learning to estimate groundwater recharge at a global scale. First, a numerical model simulates how water moves through the soil under a wide variety of climate and soil conditions. Then, a machine learning model is trained to mimic the numerical model and learn from data to estimate annual recharge rates efficiently. The model uses inputs like soil moisture and rainfall data from satellite observations, climate records, and soil properties. The key factors influencing recharge are rainfall, soil moisture, and metrics of how easily water moves through soil. The method accurately maps global recharge patterns but has some limitations in dry and irrigated areas. The results showed that the new method effectively captures realworld recharge patterns, making it a valuable tool for understanding groundwater sustainability. , Key Points A new method integrates satellite data, physicallybased models, and machine learning to estimate global groundwater recharge Rainfall, soil moisture, and unsaturated hydraulic conductivity are key predictors of groundwater recharge The method accurately captures global recharge patterns and water storage trends, aligning with independent studies",
+    "DOI": "10.1029/2025WR040312",
+    "Title": "Integrating Satellite Retrievals, Numerical Models, and Machine Learning for Global Groundwater Recharge Estimation",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
+        "UUID": "2cd0b33e-4805-4930-ba84-fef6c625a9b4",
+        "branch_id": "topic:topic_0014/term:term_0002/variable:variable_0002/variable:variable_0001/variable:variable_0002",
+        "canonical_path": "TERRESTRIAL HYDROSPHERE > GROUND WATER > GROUND WATER PROCESSES/MEASUREMENTS > AQUIFER RECHARGE > RECHARGE AMOUNT",
+        "classifier_evidence": "The article repeatedly states it estimates global groundwater recharge rate/annual recharge flux and maps recharge patterns, i.e., the amount of water replenishing aquifers.",
+        "confidence": {
+          "final": 0.96,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": null,
+          "variable_level_3": 0.96
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_3",
+        "name": "RECHARGE AMOUNT",
+        "original_candidate": null,
+        "parent_uuid": "dbeaaf9f-294c-4e45-ba9e-7be8cd449db1",
+        "path_components": [
+          "TERRESTRIAL HYDROSPHERE",
+          "GROUND WATER",
+          "GROUND WATER PROCESSES/MEASUREMENTS",
+          "AQUIFER RECHARGE",
+          "RECHARGE AMOUNT"
+        ],
+        "reason_for_stopping": "Selected Variable_Level_3 is a leaf node.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "GROUND WATER",
+        "topic": "TERRESTRIAL HYDROSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0002"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "e2a88ac8-7bf3-408c-b2b4-b3217f9e4917",
+        "branch_id": "topic:topic_0008/term:term_0007/variable:variable_0007",
+        "canonical_path": "LAND SURFACE > SOILS > HYDRAULIC CONDUCTIVITY",
+        "classifier_evidence": "Abstract identifies \u201cunsaturated hydraulic conductivity\u201d as one of the most significant/key predictors of groundwater recharge and includes soil hydraulic properties among model inputs.",
+        "confidence": {
+          "final": 0.96,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.96,
+          "variable_level_2": null,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "HYDRAULIC CONDUCTIVITY",
+        "original_candidate": null,
+        "parent_uuid": "3526afb8-0dc9-43c7-8ad4-f34f250a1e91",
+        "path_components": [
+          "LAND SURFACE",
+          "SOILS",
+          "HYDRAULIC CONDUCTIVITY"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "SOILS",
+        "topic": "LAND SURFACE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0007"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "09e712bf-7389-4980-8115-af4282469eb8",
+        "branch_id": "topic:topic_0008/term:term_0007/variable:variable_0037/variable:variable_0002",
+        "canonical_path": "LAND SURFACE > SOILS > SOIL MOISTURE/WATER CONTENT > SURFACE SOIL MOISTURE",
+        "classifier_evidence": "The study uses NASA SMAP soil moisture observations as model inputs; SMAP soil moisture retrievals correspond to near-surface soil moisture used to estimate recharge.",
+        "confidence": {
+          "final": 0.78,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": 0.78,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "SURFACE SOIL MOISTURE",
+        "original_candidate": null,
+        "parent_uuid": "bbe2ea34-8842-4a9f-9b0b-95dd3c71857f",
+        "path_components": [
+          "LAND SURFACE",
+          "SOILS",
+          "SOIL MOISTURE/WATER CONTENT",
+          "SURFACE SOIL MOISTURE"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": true,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "inferred",
+        "term": "SOILS",
+        "topic": "LAND SURFACE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0002"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          },
+          {
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+}

outputs/small_run_gpt5.5_50/cache/41f363fb4eb1433ed58ae3b13d6402b9e7dd334ad12587aaa8b0bd287714a7ff.json

@@ -0,0 +1,695 @@
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+  "identity": {
+    "DOI": "10.1002/JOC.70099",
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+  "result": {
+    "Abstract": "ABSTRACT The multiyear La Nina event of 20202023, which brought with it several climate disasters across the globe, sparked both mainstream and scientific interest in La Nina events, which typically have received less attention than El Nino. In southern Africa, there is a general expectation in the scientific community and among user groups that La Nina events result in cool and wet summers. However, such impacts do not always occur and the full diversity of La Nina impacts, including multiyear events, has not been systematically explored. Here, various temperature and rainfall characteristics occurring during three categories of La Nina eventssingle, double, and triple year events areinvestigated for the period 19702023. Spaceparameter bubble plots are used to display anomalies in midlate summer rainfall, heavy rain and dry spell frequencies, and in mean temperature and extreme heat days across four domains in southeastern Africa. Despite being relatively populated with important port cities and agriculture, not much work has focused on this region. Doubleyear La Nina summers were the least consistent in exhibiting expected wet characteristics. During tripleyear events, the subtropical domains, southern Mozambique and eastern South Africa, showed the greatest tendency towards wet conditions, although a few seasons deviated significantly in rainfall distribution from their multiyear counterparts. In contrast, the tropical domains, southern Tanzania and northern Mozambique, more consistently were cooler than average than the subtropical domains. These findings highlight the diversity of La Nina impacts on summer conditions in southeastern Africa and show that La Nina events are not necessarily associated with cooler and wetter than average summers over the region.",
+    "DOI": "10.1002/JOC.70099",
+    "Title": "La Nina Impacts on Southeastern African Climate: The Influence of Event Duration",
+    "Year": 2025,
+    "classification_outcome": "classified",
+    "classifications": [
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+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC TEMPERATURE > ATMOSPHERIC TEMPERATURE INDICES",
+        "classifier_evidence": "The abstract reports analysis of \"mean temperature and extreme heat days\" and temperature characteristics/anomalies during La Nina events; \"extreme heat days\" are a temperature-derived index.",
+        "confidence": {
+          "final": 0.79,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.79,
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+          "variable_level_3": null
+        },
+        "deterministic_validation": {
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+          "valid": true,
+          "warnings": []
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+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "ATMOSPHERIC TEMPERATURE INDICES",
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+          "ATMOSPHERIC TEMPERATURE INDICES"
+        ],
+        "reason_for_stopping": "The abstract discusses mean temperature anomalies and extreme heat day frequencies during La Nina events, but does not mention or support any of the supplied specific atmospheric temperature indices such as heat index, degree days, wind chill, or temperature concentration index.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "mixed",
+        "term": "ATMOSPHERIC TEMPERATURE",
+        "topic": "ATMOSPHERE",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+        ]
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+      {
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+        "term": "ATMOSPHERIC TEMPERATURE",
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+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0002/term:term_0014/variable:variable_0002",
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+        "classifier_evidence": "The abstract explicitly analyzes heavy rain and dry spell frequencies, which are precipitation/rainfall characteristics commonly treated as precipitation indices.",
+        "confidence": {
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+          "variable_level_1": 0.82,
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+        "deterministic_validation": {
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+          "valid": true,
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+        },
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+        "path_components": [
+          "ATMOSPHERE",
+          "PRECIPITATION",
+          "ATMOSPHERIC PRECIPITATION INDICES"
+        ],
+        "reason_for_stopping": "The abstract discusses rainfall anomalies, heavy rain frequency, and dry spell frequency during La Ni\u00f1a events, but does not mention or support any supplied specific precipitation index such as ENSO Precipitation Index, Standardized Precipitation Index, or Weighted Anomaly SPI.",
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+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "PRECIPITATION",
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+        "warnings": [
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0002/term:term_0014/variable:variable_0008",
+        "canonical_path": "ATMOSPHERE > PRECIPITATION > PRECIPITATION ANOMALIES",
+        "classifier_evidence": "The abstract states that the study displays anomalies in mid-late summer rainfall and assesses wetter-than-average or drier rainfall conditions during La Nina events.",
+        "confidence": {
+          "final": 0.9,
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+        "parent_uuid": "1532e590-a62d-46e3-8d03-2351bc48166a",
+        "path_components": [
+          "ATMOSPHERE",
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+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "PRECIPITATION",
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+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "canonical_path": "ATMOSPHERE > WEATHER EVENTS > DROUGHTS > DROUGHT FREQUENCY",
+        "classifier_evidence": "The abstract states that the study investigates anomalies in rainfall and \"dry spell frequencies\" during La Ni\u00f1a summers across southeastern Africa.",
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+        "final_status": "accepted",
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+        "support_type": "inferred",
+        "term": "WEATHER EVENTS",
+        "topic": "ATMOSPHERE",
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+        "final_status": "accepted",
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+        "name": "RAIN STORMS",
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+        "parent_uuid": "b7d562cf-9b9b-4461-900b-50423a8c4d29",
+        "path_components": [
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "support_type": "mixed",
+        "term": "WEATHER EVENTS",
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+        "warnings": [
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+      },
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+        "canonical_path": "CLIMATE INDICATORS > ATMOSPHERIC/OCEAN INDICATORS > PRECIPITATION INDICATORS > PRECIPITATION VARIABILITY",
+        "classifier_evidence": "The abstract analyzes rainfall anomalies, heavy rain frequencies, and dry spell frequencies across La Ni\u00f1a event categories, highlighting variation in wet/dry conditions over southeastern Africa.",
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+        "final_status": "accepted",
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+        "term": "ATMOSPHERIC/OCEAN INDICATORS",
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+        "warnings": [
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          },
+          {
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+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+        ]
+      },
+      {
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+        "canonical_path": "CLIMATE INDICATORS > ATMOSPHERIC/OCEAN INDICATORS > TELECONNECTIONS > EL NINO SOUTHERN OSCILLATION (ENSO)",
+        "classifier_evidence": "Title and abstract explicitly study La Nina events and compare with El Nino; La Nina/El Nino are phases of the El Nino Southern Oscillation teleconnection and its regional climate impacts.",
+        "confidence": {
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+          "variable_level_2": 0.98,
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+        },
+        "deterministic_validation": {
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+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "EL NINO SOUTHERN OSCILLATION (ENSO)",
+        "original_candidate": null,
+        "parent_uuid": "b887d3e5-4280-43d2-a34e-0f63ac086b6a",
+        "path_components": [
+          "CLIMATE INDICATORS",
+          "ATMOSPHERIC/OCEAN INDICATORS",
+          "TELECONNECTIONS",
+          "EL NINO SOUTHERN OSCILLATION (ENSO)"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC/OCEAN INDICATORS",
+        "topic": "CLIMATE INDICATORS",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
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outputs/small_run_gpt5.5_50/cache/441c79a6ace8c0ac1677e4803ee14d8a8652d0ea11245e76947821c74fa7ea24.json

@@ -0,0 +1,747 @@
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+    "Abstract": "Abstract Although extreme hourly precipitation (EHP) over the Tibetan Plateau (TP) has received growing attention, its spatiotemporal variability and the underlying mechanisms governing regional responses remain unclear. This study explores the spatiotemporal characteristics of EHP over its eastern edge during the summers of 19882023. A trend reversal is identified: the amount, intensity and frequency of EHP all decreased during 19882003 but increased in 20042023, with the amount and frequency exhibiting significant rapid growth. These changes are linked to the coevolution of the South Asian High (SAH) and Western Pacific Subtropical High (WPSH). Their joint expansion enhances EHP via favorable dynamical and thermodynamic configurations, including intensified upperlevel divergence, lowerlevel convergence, deep ascending motion, convective instability, 500 hPa specific humidity, and moisture flux convergence. Spatially, SAH expansion primarily promotes EHP over the northeastern TP via thermodynamic processes, whereas WPSH expansion mainly intensifies EHP over the southeastern TP through dynamical forcing. , Plain Language Summary The Tibetan Plateau (TP) is crucial for global water balance, yet research on extreme hourly precipitation (EHP) here remains inadequate, especially on its eastern edge, where frequent geological disasters occur. Analyzing 19882023 summer data, this study found that EHP over the TP's eastern edge decreased before 2004, but rapidly increased afterward. This change is closely linked to the coevolution of the South Asian High (SAH) and the Western Pacific Subtropical High (WPSH). Their joint expansion creates favorable dynamical and thermodynamic conditions for EHP. These findings aid in guiding disaster prevention and reduction over the TP under global warming. , Key Points Summer extreme hourly precipitation (EHP) over Tibetan Plateau (TP)'s eastern edge declined in 19882003 then significantly rose in 20042023 The change of extreme hourly precipitation is linked to coevolution of the South Asian High and Western Pacific Subtropical High There are spatial differences in the effects of the South Asian High and the Western Pacific Subtropical High",
+    "DOI": "10.1029/2025GL117722",
+    "Title": "Rapid Increase in Extreme Hourly Precipitation in Recent Two Decades in the Tibetan Plateau and Its LargeScale Drivers",
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outputs/small_run_gpt5.5_50/cache/4671daafd9a22a22b19941cd60f511ba5a8ba6351f6e07541456a0877a31fa22.json

@@ -0,0 +1,690 @@
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outputs/small_run_gpt5.5_50/cache/49ba57c39ed09e46a580cb5e0733afc2d9d2e041ad631220d6f01b5267284d41.json

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+    "Abstract": "Limited rain gauge coverage and uneven station distribution remain major challenges for hydrological analysis in ungauged watersheds. This study calibrated and evaluated GPM IMERG satellite rainfall for estimating annual maximum daily rainfall (AMS) in the Rea Watershed, Indonesia. AMS data from four rain gauge stations (Tepas, Taliwang, Seteluk, and Pototano) were used as ground observations, while nine GPM grid cells represented spatial rainfall over the watershed. A rainfall-range-based multiplicative correction was applied using gaugesatellite AMS pairs. Performance was evaluated using Percent Bias (PBIAS), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Sum of Squared Errors (SSE), followed by leave-one-station-out (LOSO) validation. Uncorrected GPM data showed systematic underestimation, with PBIAS of -17.7%, RMSE of 66.64 mm, and MAE of 48.67 mm. After optimized correction, PBIAS shifted to +13.8% and MAE slightly improved to 47.97 mm, but RMSE increased to 75.82 mm and SSE to 201,213 mm2. These results indicate that the correction reduced bias but did not improve overall accuracy. LOSO validation produced RMSE values of 50.83-72.13 mm, indicating sensitivity to station omission. Overall, GPM rainfall can support preliminary AMS estimation in ungauged areas, but its application should be treated cautiously because of persistent uncertainty in extreme rainfall estimation.",
+    "DOI": "10.29303/JPPIPA.V12I3.14532",
+    "Title": "Calibration and Evaluation of GPM Satellite Rainfall for Estimating Annual Maximum Rainfall at Ungauged Sites in the Rea Watershed",
+    "Year": 2026,
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+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+}

outputs/small_run_gpt5.5_50/cache/58c23dff190459b79392cf2c4b0a17aa43894e0f7ff2d14bafbceb4ead231d51.json

@@ -0,0 +1,371 @@
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+    "Abstract": "Ultraviolet (UV) radiation has significant implications for public health and the environment, making it crucial to understand the dynamics of UV irradiances, particularly in sensitive regions such as the southern mesoregion of Amazonas. This study aimed to analyze the seasonal variations and trends in UV irradiances (305, 310, 324, and 380 nm) in the mentioned region using remote sensing data. The data were derived from satellite-mounted sensors, covering the period from January 2005 to December 2022. The results indicate a well-defined seasonality of UV irradiances, with intensity peaks in summer and spring. The largest and smallest monthly variations in UV irradiances (305 and 310 nm) occurred in February and September, respectively, while for UV irradiances (324 and 380 nm), these variations were observed in November and September. As for the trends, the most significant findings included substantial increases in UV irradiances (324 and 380 nm) and a reduction in Cloud Optical Thickness (COT). A significant negative correlation between ozone and UV irradiance (305 nm) was also observed, along with a strong correlation between COT and UV irradiances (324 and 380 nm). The study revealed a critical situation in July, emphasizing the need for additional precautions regarding UV exposure. While the results indicate concerning behaviors in irradiances and COT, the lack of spectral UV sensors on the ground in the southern Amazon region highlights the urgent need for investment in advanced monitoring technologies so that further studies can describe these dynamics more precisely.",
+    "DOI": "10.1016/J.RSASE.2024.101423",
+    "Title": "Seasonal variations and trends in solar UV spectral irradiances based on data from the Ozone Monitoring Instrument at solar noon in Southern Amazonas, Brazil",
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+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+        "support_type": "explicit",
+        "term": "ULTRAVIOLET WAVELENGTHS",
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outputs/small_run_gpt5.5_50/cache/5fb20c1bfed31fb7926f6d2d5d2f41984087b2a3558d0a40441df54706d88657.json

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+    "Abstract": "Abstract Earth's Energy Imbalance (EEI) is a key metric to quantify climate change. While the ocean absorbs most excess heat, the atmosphere contributes only 1%2% to the longterm mean of EEI. However, our analysis of observational data demonstrates that variations in the atmosphere's energy content play a much larger role in interannual variations of EEI, especially in recent years. Including atmospheric energy uptake substantially improves agreement between observed variations in global net radiative flux at topofatmosphere (TOA) and ocean heat uptake interannually over 20052024. It also reconciles a delay between variability of these two quantities, with oceanic storage variability leading net TOA flux anomalies by 2 months. The phase shift can be explained by the atmosphere's important role in buffering and redistributing energy during El Nino Southern Oscillation. The ability to robustly diagnose these relationships is owing to continuous efforts to monitor and improve estimates of the different EEI components. , Plain Language Summary The increase of humanmade greenhouse gases in the atmosphere results in a net global energy flux at the top of the atmosphere. In the longterm mean, most of this excess heat is absorbed by the ocean due to its large thermal capacity. A comparatively small fraction warms the land, melts ice, and warms and moistens the atmosphere. However, here we show that atmospheric storage plays a nontrivial role on shorter timescales. We investigate the balance among variations in the global flux at the top of the atmosphere, the rate of atmospheric warming, and the rate of oceanic warming from year to year over the past 2 decades. We find that changes in ocean warming lead the net energy flux at the top of the atmosphere by 2 months, and these two timeseries are fairly well correlated on these interannual time scales, but the sum of atmospheric and oceanic rates of energy uptake are better correlated with a maximum correspondence at zero time lag. Hence the atmosphere is playing an important role in buffering and redistributing yeartoyear energy uptake by the climate system, most notably during El Nino and La Nina events, but especially in 2023, when surface temperatures increased remarkably. , Key Points Atmospheric and oceanic energy storage change sums correlate better to topoftheatmospheric energy fluxes than do ocean changes alone Atmospheric energy storage inclusion reconciles a 2months phase shift between topoftheatmospheric energy fluxes versus ocean changes Over the past 2 decades, interannual atmospheric energy storage variations are correlated with El Nino Southern Oscillation, but are especially large in 2023",
+    "DOI": "10.1029/2025GL119833",
+    "Title": "The Atmosphere's Substantial Role in Interannual Variability of Earth's Energy Imbalance",
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outputs/small_run_gpt5.5_50/cache/601b183c0f565103b327445edff9b677aac2ca3e665e12792ce24dc21da9f284.json

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+    "Abstract": "Abstract The Northern Indian Ocean (NIO) experiences strong upper ocean warming during the spring intermonsoon season, with nearly 90% of the days showing net heat gain. However, observations reveal spatially heterogeneous Sea Surface Temperature (SST) trends [O(1C) differences] in these regions over intraseasonal timescales (1545 days) and mesoscale and smaller length scales (<100 km), coinciding with significant lateral variability in winds [O(2 m s 1 )] and salinity stratification [O(2 g kg 1 ) in surface salinity and O(20 m in mixed layer depth)]. This study investigates the role of salinitydriven mixed layers in driving these gradients in foundational SST warming using onedimensional modeling. Simulation results using realistic surface forcing show that lateral differences in stratification result in spatial differences in warming of foundational SST by about 0.20.5C over 1421 days, specifically for shallow mixed layers. However, the influence of stratification on foundational SST warming is nuanced and varies across the NIO, leading to either enhanced or reduced warming. Idealized simulations show that this contrast depends on net heat flux and water optical properties, with stratified cases warming more under high fluxes and turbid conditions. To generalize, we derive an analytical expression for the crossover heat flux , the threshold at which stratified and unstratified cases warm equally. depends on shortwave radiation, mixed layer depth and optical properties. For representative clearsky conditions, ranges from 103 to 136 W m 2 . These findings underscore the role of salinitydriven stratification and biooptical feedback in shaping SST gradients, with likely implications for subseasonal to seasonal monsoon forecasting. , Plain Language Summary The Northern Indian Ocean undergoes intense surface warming during the spring intermonsoon season, prior to Monsoons. Observations reveal that the surface warming trend is spatially uneven over intraseasonal timescales (1545 days) and shorter length scales (<100 km). This variability coincides with significant lateral differences in wind speeds and salinity stratification. Our study shows that in shallow mixed layers, riverinedriven salinity stratification strongly influences surface warming differences. Interestingly, the impact of salinity stratification on surface warming varies in the NIO, leading to either enhanced or reduced warming. Idealized simulations show that this difference is due to the sensitivity of surface warming nature to net heat flux and water optics. Under higher heat flux and more murkier conditions, stratified cases warm more. We also derive a theoretical dailyaveraged net heat flux value at which stratified and unstratified scenarios result in the same SST warming rate. This threshold ranges from 103 to 136 W m 2 under typical clearsky conditions and tropical openocean waters, depending on water clarity and mixed layer depth. These results highlight the importance of salinitydriven stratification and biooptical feedback in modulating regional SST evolution, factors that can influence tropical cyclone intensity and monsoon predictability. , Key Points Spatial variability of O(1C) is observed during the spring intermonsoon warming in the Northern Indian Ocean Salinity stratification can enhance or suppress surface warming by up to 0.5C, depending on heat fluxes and water optics The dailyaveraged heat flux value where salinity stratification begins to amplify surface warming is quantified ( Q cross )",
+    "DOI": "10.1029/2025JC023486",
+    "Title": "Impacts of Salinity Stratification on SubSeasonal SST Warming in the Northern Indian Ocean",
+    "Year": 2026,
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outputs/small_run_gpt5.5_50/cache/6a288c8acf3dce6923048bf89029c0599843aca4e4cd02be24e2e8f553a3b9df.json

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+    "Abstract": "Abstract Tropical cyclone (TC) rainfall poses a significant threat to coastal regions, particularly in the nearstorm region, where the inner core's strongest convection occurs. Temporal variability in the nearstorm rainfall rate over a TC's lifetime, including rapid increases and decreases in rainfall rate, impacts forecast accuracy and hazard preparedness. Using multiple HighResMIP CMIP6 simulations under the SSP58.5 scenario, we find that nearstorm rainfall rate temporal variability increases under pronounced anthropogenic warming, driven primarily by elevated atmospheric moisture. Analysis of relative rainfall rate changes suggests that this heightened variability aligns with rising nearstorm rainfall rate trends. These findings have critical implications for coastal communities, highlighting not only the increased risk of nearstorm rainfall but also the rapid intensification or decline of these rainfall events. , Plain Language Summary Tropical cyclones, also known as hurricanes or typhoons, bring heavy rainfall that can cause flooding in coastal areas. Accurately predicting these rainfall patterns is essential for keeping people safe. Our study uses advanced climate models (HighResMIP CMIP6, SSP58.5 scenario) to compare rainfall in today's climate with that in a future warmer climate. We found that rainfall near a storm's center will become more unpredictable, with more frequent sudden increases and decreases in rainfall intensity. This is mainly because warmer air can hold more moisture, leading to more variable rainfall. These findings highlight increased flood risks and challenges for forecasting, urging coastal communities to prepare for more unpredictable and intense rainfall events. , Key Points Tropical cyclone nearstorm rainfall rate temporal variability, including rapid increase and rapid decrease events, increases under warming The increased rainfall rate temporal variability is driven primarily by increased atmospheric moisture Rising tropical cyclone nearstorm rainfall rate likely drives greater temporal variability",
+    "DOI": "10.1029/2025JD044655",
+    "Title": "Increasing Temporal Variability of Global Tropical Cyclone NearStorm Rainfall Under Global Warming: Insights From CMIP6 HighResMIP Simulations",
+    "Year": 2025,
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+        "reason_for_stopping": "The abstract discusses increased atmospheric moisture as a driver of tropical cyclone nearstorm rainfall variability, but it does not identify or analyze any specific humidity index, temperature-humidity index, or water vapor transport index from the supplied child candidates.",
+        "review_required": true,
+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC/OCEAN INDICATORS",
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+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+}

outputs/small_run_gpt5.5_50/cache/78fce8da8fe337b2544e966f067c7458dda3e498d31a83d065624fc91495f9d7.json

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+    "Abstract": "Abstract We use the directional derivative approach to estimate hourly emissions from measured by the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. The hourly scanning capacity of TEMPO enables the inclusion of the tendency term, relaxing the steadystate assumption that limits previous satellitebased methods. The directional derivative emission estimator is calculated on the local coordinates of TEMPO Level2 pixels and consequently oversampled to 1km grids. These innovations enable integration of emission estimators over km elliptical footprints centered at 14 power plants in the US, resulting in over 15,500 hr of emission rates in the first 17 months of TEMPO operation. TEMPObased emission rates are compared with measurements from stackmounted Continuous Emissions Monitoring Systems (CEMS). Inclusion of the tendency term improves correlations in 10 out of 14 power plants and the overall correlations at hourly, daily, and monthly scales. The TEMPObased emission rates are around four times lower than CEMS, highlighting the challenges to accurately retrieve vertical column and to quantify / ratio near strong point sources. , Plain Language Summary Nitrogen oxides, primarily emitted by power plants and vehicles, are major air pollutants that create smog, contribute to acid rain, and affect climate. Thus, monitoring nitrogen oxides emission in real time is crucial for environmental protection and human health. This study uses data from a geostationary satellite instrument, Tropospheric Emissions: Monitoring of Pollution (TEMPO), which measures the vertically integrated column amount of nitrogen dioxide every hour across North America. We applied directional derivative approach, a method to estimate hourly nitrogen oxides emissions directly from the TEMPO observations. Our method accounts for how nitrogen oxides emissions change throughout the day, something previous satellitedatadriven approaches couldn't capture. We compared the estimated nitrogen oxides emissions with smokestack measurements from power plants across the United States. Our results showed that considering the hourly changes of nitrogen dioxide significantly improved the emission estimation. We demonstrate that the unprecedented spatiotemporal resolution of TEMPO measurements coupling with our method can be a powerful tool to track emission with near real time capability, supporting more effective environmental monitoring and policy decisions. , Key Points First demonstration of hourly nitrogen oxides emission from Tropospheric Emissions: Monitoring of Pollution (TEMPO) measurements compared against facilitylevel continuous emission data Incorporating nitrogen dioxide column tendency significantly improves emission estimation and enhances correlation with facilitylevel data Nitrogen oxides emission is estimated using the directional derivative approach from Level 2 data preserving TEMPO's native spatial pattern",
+    "DOI": "10.1029/2025JD044565",
+    "Title": "Hourly Nitrogen Oxides Emissions Estimated From TEMPO and Comparison With FacilityLevel Monitoring Data",
+    "Year": 2025,
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+        "classifier_evidence": "Title and abstract repeatedly state estimation of hourly emissions and emission rates from TEMPO, compared with smokestack CEMS measurements at power plants.",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC CHEMISTRY",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+        "classifier_evidence": "Abstract states TEMPO measures the vertically integrated column amount of nitrogen dioxide every hour and that nitrogen dioxide column tendency improves emission estimation.",
+        "confidence": {
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+          "ATMOSPHERE",
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC CHEMISTRY",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
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+        "support_type": "inferred",
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+}

outputs/small_run_gpt5.5_50/cache/7a257c50c4dc158c83937bebf736252d07beec8e5f217ccc8f7df6221c3de79b.json

@@ -0,0 +1,1239 @@
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+    "Abstract": "This study elucidates the mechanism by which Arctic-cooling-induced inner-continental warming over East Asia suppressed July precipitation extremes during 20132019 along the quasi-stationary rainy front in the outer-continental region encompassing eastern China, the Yellow Sea, and Korea (eCYK). Spatiotemporal variations in precipitation were analyzed using IMERG satellite data. At the same time, large-scale circulation, coupled with microphysical aerosolcloud interactions and cloud properties, was investigated using ERA5 reanalysis, Terra-MODIS observations, and WRF-Chem simulations. Despite a general increase in summer precipitation and more frequent extreme events across the eCYK region, a marked weakening of extreme ascent was observed from 2013 to 2019, contributing to widespread drought conditions associated with pronounced Arctic cooling and intensified inner-continental warming. The warming-induced expansion of the upper-tropospheric Tibetan High strengthened the East Asian jet along its northern flank, while the southward intrusion of polar air contrasted with it. The resulting anomalous northwesterly flow over the eCYK region, situated near the East Asian jet exit, was further amplified by indirect circulation, which disrupted the transport of moisture from the tropical oceans surrounding Southeast Asia toward the rainy front. Terra-MODIS cloud observations revealed reduced convective cloud formation, which directly led to weaker precipitation extremes. Furthermore, WRF-Chem simulations showed that microphysical processes driven by anthropogenic aerosol indirect effects reduced precipitation by approximately 5.6 % across the eCYK region in July 2018. The combined impact of inner-continental warming and enhanced aerosol formation suppressed convective cloud development, thereby delaying the onset and reducing the intensity of extreme precipitation events.",
+    "DOI": "10.1016/J.ATMOSRES.2025.108645",
+    "Title": "On the dynamic link between summer inner-continental warming and the outer-continental weakened precipitation extreme ascent in East Asia",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+      {
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+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WINDS",
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+      {
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+        ]
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+          "CLOUD MICROPHYSICS"
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+      {
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+      {
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+      },
+      {
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+        "support_type": "explicit",
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+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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outputs/small_run_gpt5.5_50/cache/7ee711c8d53b7c8f7eee52fea443d50064819f764e1056f2ce21bb9a33499099.json

@@ -0,0 +1,386 @@
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+    "Abstract": "Abstract Mesoscale convective systems (MCSs) are large, organized convective storms that frequently produce flash floods and other severe hazards such as damaging winds, hail, and tornadoes. Developing an observationally based MCS hazard climatology is important for establishing a baseline to evaluate the representation of these events in numerical models. This study constructs such a climatology using a 13year MCS data set, storm reports, and atmospheric reanalysis. MCSrelated and nearstorm environmental variables are extracted and used to train objectbased machine learning (ML) models. Three models are developed to predict flash floods, severe (including all wind, hail and tornado events), and significantsevere events, with the latter representing higherimpact hazards. The flash flood and severe models perform well in distinguishing hazardproducing MCSs from nonproducing ones, while the significantsevere model shows limited skill, likely due to sample size constraints. The flash flood and severe models are then applied to the full MCS archive to reconstruct a more complete warm season hazard climatology, addressing the potential underreporting in storm reports and gaps in flash flood reports during early years. The study also examines the application of these models to convectionpermitting model simulations. While the spatial distribution of simulated MCS hazards generally aligns with observations, event frequencies differ considerably. These discrepancies are attributed to biases in both the derived input variables and the representation of MCS properties within the model. , Plain Language Summary Mesoscale convective systems (MCSs) are large, organized thunderstorms that produce hazardous weather, including flash floods, damaging winds, hail, and tornadoes. This study creates a 13year climatology of MCSrelated hazards by developing machine learning models trained on observational and atmospheric data. Three separate models were built: one for flash floods, one for all types of severe weather (wind, hail, and tornadoes), and one for the most intense severe events. The models for flash floods and general severe weather worked well, while the model for the most intense events had more difficulty, likely because these cases are rarer. The study also tested whether the same prediction models could be used with output from highresolution model simulations. While the simulations showed similar storm patterns in space, the number of predicted hazardous events did not always match realworld observations. This mismatch was due to differences in both the simulated weather conditions and how the storms themselves were represented in the model. Overall, the study shows that machine learning can help fill gaps in severe weather records and improve our understanding of storm hazards. , Key Points Developed objectbased machine learning (ML) models to predict mesoscale convective system (MCS)produced hazards Created a more complete MCS hazard climatology for the warm season by correcting historical data gaps and storm underreporting ML applied to ConvectionPermitting Models reveals biases in simulated MCSs and predictors lead to inaccurate hazard frequency",
+    "DOI": "10.1029/2025JD045335",
+    "Title": "A Climatology of Mesoscale Convective System Hazards in the United States and Their Representation in a ConvectionPermitting Model",
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+          "ATMOSPHERE",
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outputs/small_run_gpt5.5_50/cache/86b29bebea66d08a0ce587fedefe61af9d42499380213492b37b8f5122c8752b.json

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+    "Abstract": "Abstract. More and more wind farms are planned and built in regions prone to tropical cyclones. However, the current International Electrotechnical Commission (IEC) standard provides no clear guidelines on how to account for turbulence occurring during tropical cyclones. This study investigates how well the Mann uniform shear model, a model referenced by the IEC, can model turbulence during tropical cyclone conditions. We analyzed sonic anemometer measurements at 60 m from four typhoon cases in the South China Sea. The Mann model was fit to the one-point spectra in different locations in the typhoon structure. We found that the Mann model can fit the observed spectra outside the typhoon eye and the rainbands to a certain extent. However, several deficiencies are identified. (1) In the outer-cyclone region, spectral energy at wavenumbers smaller than 310-3 m1 is generally larger than predicted by the Mann model, likely reflecting the presence of mesoscale wind fluctuations. (2) Consistent with previous studies, excess spectral energy is observed at wavenumbers larger than 101 m1 in the inner-cyclone and eyewall regions of one typhoon; however, it cannot be ruled out that this excess energy may be related to measurement quality. (3) In the inner-cyclone region, the peak wavenumbers of the alongwind and crosswind spectra are often closer together than predicted by the Mann model. In these cases, the crosswind component exhibits larger-than-predicted spectral energy within the energy-containing subrange. This study can serve as a baseline for further research addressing turbulence in tropical cyclones in the context of structural engineering.",
+    "DOI": "10.5194/WES-11-961-2026",
+    "Title": "How well can the Mann model describe typhoon turbulence?",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+        "review_required": true,
+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC WINDS",
+        "topic": "ATMOSPHERE",
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+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+      {
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+        "branch_id": "topic:topic_0002/term:term_0011/variable:variable_0004/variable:variable_0008",
+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC WINDS > WIND DYNAMICS > TURBULENCE",
+        "classifier_evidence": "Title asks how well the Mann model can describe typhoon turbulence; abstract repeatedly states the study investigates and analyzes turbulence during tropical cyclone/typhoon conditions using sonic anemometer spectra.",
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+        "branch_id": "topic:topic_0002/term:term_0016/variable:variable_0017",
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+        "term": "WEATHER EVENTS",
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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outputs/small_run_gpt5.5_50/cache/9dfd1c9d4f89223cd8be90eadcf8a1dcadc7b9451dbac4949ac7ddd537bfbac3.json

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+    "Abstract": "With the intensification of climate change and environmental pollution, reducing carbon monoxide (CO) emissions has become a focal point global governance. Existing research often relies on site-specific observational data, limiting continuous spatiotemporal analysis while failing to quantify impacts complex factors, such as vegetation structure. This study integrated passive active satellite remote sensing sensors with aerodynamic models analyze distribution contributions CO reduction across China from 2013 2022. extends traditional site-scale spatial scale, enhancing model accuracy through high-resolution data. Our findings indicate that in regions east Hu-line primarily contributes dry deposition, cumulative deposition 5.2854 million tons, improving air quality by 0.00216% yielding economic benefits 7.436 billion USD. Forest-type nearly ten times more than non-forest types, significant differences. Evergreen broadleaf trees contribute most, 2.7826 tons. Wavelet transform coherence identifies leaf area index (LAI) parameter highest all time-frequency scales, averaging wavelet 0.79. The SHapley Additive exPlanations (SHAP) indicates temperature is main factor influencing temporal fluctuations. These results provide deeper understanding vegetation's role cycle, offering implications for policies, management, ecosystem service enhancement globally.",
+    "DOI": "10.1016/J.SRS.2026.100415",
+    "Title": "Quantifying the impact of vegetation on carbon monoxide reduction using multi-source remote sensing in China",
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+}

outputs/small_run_gpt5.5_50/cache/a3840a519e6e2903dbf3b42267148e962e86bf3197cd8628feb6544d658dac6f.json

@@ -0,0 +1,270 @@
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+    "Abstract": "Abstract We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ( $$r^2=0.6$$ r 2 = 0.6 ) to high-quality airborne estimates of sun-induced fluorescence (SIF)SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESAs upcoming FLEX mission we develop a method for SIF retrieval in the O $$_2$$ 2 -A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of $$0.78 \\, \\, \\mathrm {mW\\, nm^{-1} \\, sr^{-1} \\, m^{-2}}$$ 0.78 mW nm - 1 sr - 1 m - 2 .",
+    "DOI": "10.1007/978-3-031-91835-3_6",
+    "Title": "Retrieval of Sun-Induced Plant Fluorescence in the O$$_2$$-A Absorption Band from DESIS Imagery",
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outputs/small_run_gpt5.5_50/cache/af6b67c689e40ffbd38dbebbc500334bf6ae5157713593b167e108fb19272259.json

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+  "result": {
+    "Abstract": "Abstract The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundaryforcing techniques can be useful to maintain the largescale features from the coarseresolution global models in agreement with the inner modes of the higherresolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Modeloriginally developed at the National Centers for Environmental Predictionemploys a newly reformulated scaleselective bias correction, together with the 3hourly assimilation of the satellitebased precipitation estimates constructed from the Climate Prediction Center morphing technique. The twoscheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satelliteenhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas. , Key Points Blending two boundaryforcing techniques to improve dynamical downscaling Assimilation of satellitebased precipitation estimates for extremeevent analysis Dynamical downscaling applied in regional hydroclimate reconstructions",
+    "DOI": "10.1002/2016JD024927",
+    "Title": "Satelliteenhanced dynamical downscaling for the analysis of extreme events",
+    "Year": 2016,
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+        "canonical_path": "ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT > 3 AND 6 HOUR PRECIPITATION AMOUNT",
+        "classifier_evidence": "Abstract states the method uses \u201c3hourly assimilation of the satellitebased precipitation estimates,\u201d supporting a 3-hour precipitation amount temporal category.",
+        "confidence": {
+          "final": 0.76,
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+          "topic": null,
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+        "name": "3 AND 6 HOUR PRECIPITATION AMOUNT",
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+        "term": "PRECIPITATION",
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+      {
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+          "WEATHER EVENTS",
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+        "term": "WEATHER EVENTS",
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outputs/small_run_gpt5.5_50/cache/b8d0f10c86e5be96342b0cfe8c038d8e800f8269ebfbc6aee7a2e87ac36dac70.json

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+  "identity": {
+    "DOI": "10.1016/J.APGEOG.2026.104008",
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+    "Abstract": "Ecological restoration project (ERP) plays a pivotal role in enhancing ecosystem services (ES) and promoting regional sustainable development, yet a quantitative understanding of how multiple restoration measures (ERMs) affect ES under interacting influence of climatic conditions remains limited. In this study, we developed an analytical framework based on explainable machine learning to disentangle the independent contribution and interactive effects of nine ERMs and climatic variables on changes in five ESs in the Yimeng Mountains ERP. Results suggest that: within the short-term assessment period (2020-2023), (1) ERMs exhibited heterogeneous impacts on ES provision, with afforestation measure showing the greatest overall enhancement of ES, whereas forest tending measure exhibited the strongest independent contribution to ES variation and pronounced interactions with climatic factors; (2) the implementation of ERMs was associated with intensified both synergies and trade-offs among individual ES without altering their original relationship directions, with the strongest trade-off between wind erosion prevention and water conservation strengthening from 0.32 to 0.78; and (3) Climatic drivers explained 6788% of the observed variability in ES indicators, highlighting the important role of climateERMs interactions in shaping restoration outcomes. This study introduces a fine-scale analytical perspective at the measure level and an explainable framework for disentangling climate-restoration interactions, supporting iterative refinement of restoration strategies and climate-adaptive management.",
+    "DOI": "10.1016/J.APGEOG.2026.104008",
+    "Title": "Disentangling the impact of climate and ecological restoration measures on ecosystem service: A case study in Yimeng mountains",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+        "canonical_path": "BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS",
+        "classifier_evidence": "Study quantifies changes in ecosystem services/provision and indicators such as water conservation and wind erosion prevention under restoration measures and climate drivers, which are ecosystem processes/functions.",
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+        "final_status": "accepted",
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+        "name": "ECOSYSTEM FUNCTIONS",
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+        "reason_for_stopping": "The article addresses ecosystem services/functions broadly under ecological restoration and climate interactions, with examples such as wind erosion prevention and water conservation, but it does not provide explicit support for any supplied direct-child ecosystem-function category such as carbon sequestration, primary production, nutrient cycling, decomposition, or food-web dynamics.",
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+        "review_status": "not_required",
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+        "term": "ECOLOGICAL DYNAMICS",
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+        "classifier_evidence": "The abstract identifies forest-related restoration measures, including afforestation and forest tending, as key drivers affecting ecosystem services.",
+        "confidence": {
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+        "name": "FORESTS",
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+        "parent_uuid": "9361962c-cfc7-4428-8843-b3502718c382",
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+          "BIOSPHERE",
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+          "TERRESTRIAL ECOSYSTEMS",
+          "FORESTS"
+        ],
+        "reason_for_stopping": "The abstract supports forest-related ecological restoration measures such as afforestation and forest tending, but does not specify a forest biome/type such as boreal, temperate deciduous/mixed/coniferous, tropical, or rainforest.",
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+        "review_status": "not_required",
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+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+        "classifier_evidence": "The abstract focuses on ecological restoration projects/measures and their effects on ecosystem services, indicating ecosystems shaped by human management interventions.",
+        "confidence": {
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+}

outputs/small_run_gpt5.5_50/cache/bbf70f7a80454103ac046794850ee63aa0cb704a38a2a7e38a6d5bb0df0774c5.json

@@ -0,0 +1,421 @@
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+    "DOI": "10.1016/J.ATMOSRES.2026.108937",
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outputs/small_run_gpt5.5_50/cache/bca153aa6b225d8d9f0a63dbc3233fe72ea930ce4cdfc58254af42532ef6c1d6.json

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+    "Abstract": "Abstract Hail and lightning, associated with severe convective storms, can cause extensive damage to infrastructure, agriculture, and ecosystems. Because of the small scale of these storms and the complexity of the involved processes, observing and modeling convective storms is challenging. The potential of online diagnostics in convectionpermitting models to simulate hail and lightning, especially over climatic time scales and extended regions, has not yet been fully exploited. To address this gap, we present a Europeanwide hail and lightning climatology (20112021) using the Consortium for Small Scale Modeling (COSMO) regional climate model with a horizontal grid spacing of 2.2 km, coupled with a hail growth model (HAILCAST) and the lightning potential index (LPI) diagnostics. We further developed a new Europeanwide hail product based on the Operational Program for the Exchange of Weather Radar Information (OPERA) composite. Model validation against observations demonstrates an overall good performance in simulating hail and lightning on spatial, seasonal, and diurnal scales. The highest hail frequencies occur during summer along the slopes of high mountain ridges, such as the Alps, Pyrenees, and the Carpathians, aligning with observed lightning hotspots in Europe. In autumn, hail and lightning occur predominantly over the Mediterranean and along the Adriatic coast. Severe hail events with a maximum hail diameter larger than 20 mm mainly occur in the Po Valley, western Spain, and Eastern Europe. This 11year simulation provides a Europeanwide data set of severe convective storms and their properties, serving as a basis for further studies of convective events and their impacts. , Plain Language Summary Severe convective storms often cause hail and lightning, which can lead to significant damage. Such events are difficult to observe and model due to their small scale and complexity. To address this, we performed an 11year long highresolution climate simulations (2.2 km) using COSMO with hail and lightning diagnostics. Additionally, we developed a Europeanwide hail data set based on the Operational Program for the Exchange of Weather Radar Information radar composite. The model was validated against radar and crowdsourced data, demonstrating good performance. Hail and lightning are most frequent along mountain ranges such as the Alps and Pyrenees in summer, and in autumn, storms shift to the Mediterranean. This 11year data set offers valuable insights into the characteristics of severe storms across Europe, supporting further research on factors driving these events and their impact in the context of climate change. , Key Points We present a Europeanwide climatology of hail and lightning based on a convectionpermitting climate simulation We introduce a new radarbased Europeanwide hail data set for the years 20132021",
+    "DOI": "10.1029/2024JD042828",
+    "Title": "A European Hail and Lightning Climatology From an 11Year KilometerScale Regional Climate Simulation",
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+        ]
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+    "errors": [],
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+    "processing_metadata": {
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+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/c50377db2b63e7a0926aec4e795a86c661828da96d3c909caf314eeae8edd131.json

@@ -0,0 +1,276 @@
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+  "identity": {
+    "DOI": "10.1029/2025MS005289",
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+    "Abstract": "Abstract Our goal in this study is to characterize the relationship between lower tropospheric environmental humidity and convective mass flux in the tropics. To do so, we have created gridded convective mass flux data sets from five global stormresolving models (GSRMs). We have three principal findings. First, in humid environments, mass flux increases with height from the surface through the depth of the lower free troposphere, forming a deepinflow. In dry environments, mass flux does not increase with height in the lower free troposphere. Second, midtropospheric mass flux increases nonlinearly with increasing lower tropospheric humidity, resembling a widely reported pickup in tropical precipitation. Third, increased lower tropospheric humidity is associated with reduced updraft buoyancy. To interpret these findings, we employ a simple threeequation parcel model with stochastic entrainment. The parcel model suggests that the response of convective mass flux to lower tropospheric humidity is governed by two effects: (a) survival, in which a greater share of entraining parcels ascend rather than detrain with greater humidity; and (b) dilution, in which the average entrainment rate among surviving parcels increases with environmental humidity. Together, survival and dilution account for the three mass flux responses to humidity. , Plain Language Summary This study aims to quantify and understand the rate at which air mass ascends within cumulus and cumulonimbus clouds in the tropics. This rate is known as convective mass flux . Using finescale supercomputer simulations of Earth's atmosphere, we find that convective mass flux is extremely sensitive to humidity in the lowest few kilometers of the atmosphere. Greater humidity leads to greater convective mass flux. We then test whether environmental humidity increases mass flux by making cloudy air more buoyant (i.e., less dense relative to its surroundings). We reject this hypothesis, finding instead that the opposite occurs: Greater humidity is associated with cloudy air which is less buoyant. To make sense of these results, we use a simple set of equations to simulate cloudy air as it rises and ingests dry environmental air. As the environment becomes more humid, cloudy air may absorb a greater mass of environmental air without drying out and halting its ascent. This causes mass flux to increase with humidity and causes convective clouds' average density to become more like that of their environment. , Key Points Convective mass flux transitions to deep inflow and increases quasiexponentially as lower tropospheric relative humidity increases Greater lower tropospheric humidity is not associated with greater populationmean buoyancy in convective updrafts These phenomena are the result of increasing survival and dilution among convectively active parcels of air",
+    "DOI": "10.1029/2025MS005289",
+    "Title": "How Convective Mass Flux Responds to Environmental Humidity",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
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+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR INDICATORS > HUMIDITY > RELATIVE HUMIDITY",
+        "classifier_evidence": "The abstract key points state that convective mass flux increases as \"lower tropospheric relative humidity increases,\" directly identifying relative humidity as the humidity variable studied.",
+        "confidence": {
+          "final": 0.92,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": null,
+          "variable_level_3": 0.92
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_3",
+        "name": "RELATIVE HUMIDITY",
+        "original_candidate": null,
+        "parent_uuid": "427e5121-a142-41cb-a8e9-a70b7f98eb6a",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC WATER VAPOR",
+          "WATER VAPOR INDICATORS",
+          "HUMIDITY",
+          "RELATIVE HUMIDITY"
+        ],
+        "reason_for_stopping": "Selected Variable_Level_3 is a leaf node.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WATER VAPOR",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
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+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
+        ]
+      },
+      {
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+        "branch_id": "topic:topic_0002/term:term_0012/variable:variable_0002/variable:variable_0002",
+        "canonical_path": "ATMOSPHERE > CLOUDS > CLOUD DYNAMICS > IN-CLOUD VERTICAL VELOCITIES",
+        "classifier_evidence": "The study centers on convective mass flux, described as the rate at which air mass ascends within cumulus and cumulonimbus clouds, and how it varies with height and humidity.",
+        "confidence": {
+          "final": 0.76,
+          "term": null,
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+          "variable_level_2": 0.76,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "IN-CLOUD VERTICAL VELOCITIES",
+        "original_candidate": null,
+        "parent_uuid": "62019831-aaba-4d63-a5cd-73138ccfa5d0",
+        "path_components": [
+          "ATMOSPHERE",
+          "CLOUDS",
+          "CLOUD DYNAMICS",
+          "IN-CLOUD VERTICAL VELOCITIES"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": true,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "inferred",
+        "term": "CLOUDS",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0002"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          },
+          {
+            "DOI": null,
+            "code": "REVIEW_RECOMMENDED_WEAK_SUPPORT",
+            "details": {
+              "confidence_final": 0.76,
+              "level": "Variable_Level_2",
+              "support_type": "inferred"
+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
+            "stage": "review_flagging"
+          }
+        ]
+      },
+      {
+        "UUID": "7c4d5f8f-4809-4859-b379-3b8c379bc83c",
+        "branch_id": "topic:topic_0002/term:term_0012/variable:variable_0007/variable:variable_0002",
+        "canonical_path": "ATMOSPHERE > CLOUDS > CONVECTIVE CLOUDS/SYSTEMS (OBSERVED/ANALYZED) > CUMULONIMBUS",
+        "classifier_evidence": "Plain language summary states the study quantifies air mass ascent within \"cumulus and cumulonimbus clouds\" in the tropics, known as convective mass flux.",
+        "confidence": {
+          "final": 0.75,
+          "term": null,
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+          "variable_level_2": 0.75,
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+        },
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+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "CUMULONIMBUS",
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+        "parent_uuid": "9a802ef3-680d-4bc6-a42e-aa84d5eb9908",
+        "path_components": [
+          "ATMOSPHERE",
+          "CLOUDS",
+          "CONVECTIVE CLOUDS/SYSTEMS (OBSERVED/ANALYZED)",
+          "CUMULONIMBUS"
+        ],
+        "reason_for_stopping": "The article discusses convective mass flux in cumulus and cumulonimbus clouds generally, with no evidence specifying cumulonimbus calvus, cumulonimbus capillatus/anvil clouds, or pyrocumulonimbus/fire-generated storms.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "CLOUDS",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "e1dff4d5-2e5b-46e7-9804-9de29fdb36d9",
+        "branch_id": "topic:topic_0002/term:term_0012/variable:variable_0007/variable:variable_0003",
+        "canonical_path": "ATMOSPHERE > CLOUDS > CONVECTIVE CLOUDS/SYSTEMS (OBSERVED/ANALYZED) > CUMULUS",
+        "classifier_evidence": "Plain language summary states the study quantifies air mass ascent within \"cumulus and cumulonimbus clouds\" in the tropics, known as convective mass flux.",
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+          "final": 0.75,
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+          "CLOUDS",
+          "CONVECTIVE CLOUDS/SYSTEMS (OBSERVED/ANALYZED)",
+          "CUMULUS"
+        ],
+        "reason_for_stopping": "The article discusses convective mass flux in cumulus and cumulonimbus clouds broadly, without evidence for a specific cumulus subtype such as congestus, humilis, mediocris, castellanus, or pyrocumulus.",
+        "review_required": false,
+        "review_status": "not_required",
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+        "topic": "ATMOSPHERE",
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          }
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+        "timeout_seconds": 30.0
+      },
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+        "topic": "topic-v1",
+        "variable": "variable-v1"
+      },
+      "run_id": "manual-small-run",
+      "started_at": "2026-07-11T21:33:43.341158+00:00",
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+    },
+    "processing_status": "completed",
+    "review_status": "not_required",
+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/c655b9755f9867a0cd65cc2fdfb8791fb833ad20b30b843cfe22d3309cd13458.json

@@ -0,0 +1,224 @@
+{
+  "cache_key": "c655b9755f9867a0cd65cc2fdfb8791fb833ad20b30b843cfe22d3309cd13458",
+  "identity": {
+    "DOI": "10.1029/2024JB030689",
+    "application_version": "0.1.0",
+    "article_fingerprint": "d6311b053ecf3bef7fdab98502f440b743c82b8120a6b52af912ae786e3bb3d8",
+    "configuration_hash": "92d5193323468144f8da5f7c8629eb8dca34a0b1a1b0d4b74b09ee2b478c90fc",
+    "model_max_retries": 2,
+    "model_name": "gpt-5.5",
+    "model_provider": "openai",
+    "model_temperature": 0.0,
+    "model_timeout_seconds": 30.0,
+    "prompt_version_term": "term-v1",
+    "prompt_version_topic": "topic-v1",
+    "prompt_version_variable": "variable-v1",
+    "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
+  },
+  "result": {
+    "Abstract": "Abstract Ongoing climate change is leading to an increase in prolonged droughts and severe weather events, which are particularly pronounced in semiarid regions, such as the western United States. These extremes could have lasting social and environmental impacts. Continuous monitoring of nearsurface hydrological processes and groundwater resources provides helpful information for effective water resource management. The seismological signature of groundwater fluctuations is clear in the temporal variations in seismic velocities, dv/v. To this end, developing a proxy for groundwater level using dv/v represents an opportunity, but further understanding of the relation between dv/v and subsurface hydrology is required. In this study, we apply singlestation crosscomponent correlation analysis to 28 broadband seismic stations in Utah between January 2006 and March 2023 and analyze the dv/v in the 24 Hz frequency band. To explain dv/v, we linearly superimpose thermoelastic stresses, soil moisture estimated from remote sensing data products, and a longterm deep water table pore pressure. We find that the relative contributions of each depend on the location. Still, adding a longterm water table decline, which is not systematically observed in soil moisture, better fits our data. We conclude that soil moisture alone does not explain the variations in total water storage when subsurface moisture is decoupled from the deepwater table. We also conclude that dv/v can be used as a proxy for water storage. , Plain Language Summary Climate change makes droughts and water shortages more common in the western United States. Tracking how water moves and is stored underground is essential for understanding these changes, but traditional tools such as monitoring wells and satellites cover too little ground or lack enough detail. In this study, we used small changes in the speed of seismic waves, dv/v, recorded at 28 seismic stations across Utah over more than a decade. These subtle changes help reveal how water flows and accumulates below the surface. By combining dv/v data with estimates of soil moisture and longterm lake level changes (used as a proxy for groundwater), we built a model to separate the effects of shallow and deepwater processes. We found that dv/v can detect seasonal and longterm shifts in subsurface water, and deep groundwater changes are significant in some areas. This seismic method offers a powerful new way to monitor underground water across large regions, bridging the gap between scattered well data and coarse satellite data. Our approach may help scientists and water managers better understand and adapt to changing water availability in a warming climate. , Key Points Multiyear wetdry water cycles are closely consistent with dv/v observations, notably at stations within the Great Salt Lake watershed The annual dv/v variations and their peak times closely correspond to expected water cycle patterns in Utah Using longterm lake level as a groundwater proxy in modeling reveals regional recharge timing differences driven by elevation and snowmelt",
+    "DOI": "10.1029/2024JB030689",
+    "Title": "A Decadal Survey of the NearSurface Seismic Velocity Response to Hydrological Variations in Utah, United States",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
+        "UUID": "e3fa1998-b003-4d55-a92e-16b42ac0fc17",
+        "branch_id": "topic:topic_0011/term:term_0001",
+        "canonical_path": "SOLID EARTH > EARTH GASES/LIQUIDS",
+        "classifier_evidence": "The study focuses on near-surface hydrological processes, groundwater fluctuations, soil moisture, pore pressure, water-table decline, and subsurface water storage inferred from seismic velocity changes.",
+        "confidence": {
+          "final": 0.83,
+          "term": 0.83,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": null,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Term",
+        "name": "EARTH GASES/LIQUIDS",
+        "original_candidate": null,
+        "parent_uuid": "2b9ad978-d986-4d63-b477-0f5efc8ace72",
+        "path_components": [
+          "SOLID EARTH",
+          "EARTH GASES/LIQUIDS"
+        ],
+        "reason_for_stopping": "The article discusses subsurface water, groundwater, soil moisture, and water storage; none of the supplied direct-child candidates (hydrogen gas, natural gas, petroleum) are supported.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "mixed",
+        "term": "EARTH GASES/LIQUIDS",
+        "topic": "SOLID EARTH",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "term_0001"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "e89dc20d-0570-41ca-8039-38316332238a",
+        "branch_id": "topic:topic_0014/term:term_0002/variable:variable_0002/variable:variable_0001/variable:variable_0003",
+        "canonical_path": "TERRESTRIAL HYDROSPHERE > GROUND WATER > GROUND WATER PROCESSES/MEASUREMENTS > AQUIFER RECHARGE > RECHARGE FREQUENCY",
+        "classifier_evidence": "The abstract reports annual water-cycle patterns and states that modeling reveals regional recharge timing differences driven by elevation and snowmelt.",
+        "confidence": {
+          "final": 0.55,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": null,
+          "variable_level_3": 0.55
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_3",
+        "name": "RECHARGE FREQUENCY",
+        "original_candidate": null,
+        "parent_uuid": "dbeaaf9f-294c-4e45-ba9e-7be8cd449db1",
+        "path_components": [
+          "TERRESTRIAL HYDROSPHERE",
+          "GROUND WATER",
+          "GROUND WATER PROCESSES/MEASUREMENTS",
+          "AQUIFER RECHARGE",
+          "RECHARGE FREQUENCY"
+        ],
+        "reason_for_stopping": "Selected Variable_Level_3 is a leaf node.",
+        "review_required": true,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "inferred",
+        "term": "GROUND WATER",
+        "topic": "TERRESTRIAL HYDROSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0003"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          },
+          {
+            "DOI": null,
+            "code": "REVIEW_RECOMMENDED_WEAK_SUPPORT",
+            "details": {
+              "confidence_final": 0.55,
+              "level": "Variable_Level_3",
+              "support_type": "inferred"
+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
+            "stage": "review_flagging"
+          }
+        ]
+      },
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outputs/small_run_gpt5.5_50/cache/c876c524b8750e942b7cf0e6da9c3cf9476fd9aed29ba411562386b8c3d8b429.json

@@ -0,0 +1,576 @@
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outputs/small_run_gpt5.5_50/cache/ccc59d6780f06d2aabce86287039348ae2e1e0e3cda74fd0d8ba69d06972db6d.json

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+    "Abstract": "Cloud-to-ground (CG) lightning is the main ignition source for wildfires in boreal forests. Characterizing the environmental controls on both CG lightning occurrence and ignition success is therefore essential for developing effective early warning systems. However, previous analyses of conditions favorable for lightning often involved the incorporation of non-igniting intracloud (IC) lightning because of data limitations, creating a significant knowledge gap. This gap is particularly acute in the Greater Khingan Mountains (GKM) of China, which contain largest boreal forest and experience the highest frequency of lightning-ignited wildfires (LIWs). In this study, meteorological, atmospheric, aerosol, and land-surface datasets from 2019 to 2023, along with lightning records and LIW occurrences, are synthesized to distinguish igniting lightning and identify the key determinants of CG lightning occurrences and ignition success. The results indicated that CG lightning occurs preferentially in higher-elevation mountainous areas that are dominated by needle-leaved deciduous forests. Statistical analyses revealed significant (p < 0.01) associations between CG lightning occurrences and elevated aerosol loadings, higher temperatures, greater precipitation levels, and enhanced vertical atmospheric instability. Furthermore, compared with non-igniting CG lightning, igniting lightning is associated with greater atmospheric instability and stronger updrafts, along with significantly warmer and drier land-surface conditions that promote fuel aridity. Collectively, these findings establish a conceptual framework for identifying high-risk lightning events and achieving enhanced early targeted LIW warning capabilities.",
+    "DOI": "10.1016/J.ATMOSRES.2026.108919",
+    "Title": "Environments conducive to cloud-to-ground and ignited lightning in a boreal forest of Northeast China",
+    "Year": 2026,
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+        "support_type": "inferred",
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outputs/small_run_gpt5.5_50/cache/ce8ea9fa9959cf5f1e273f6315b93c9c541ebe4cd6c8a55f2fc801cec4178edb.json

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+    "DOI": "10.1016/J.AOSL.2021.100035",
+    "Title": "The exceptionally strong and persistent Arctic stratospheric polar vortex in the winter of 20192020: 2019-2020",
+    "Year": 2021,
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+        "deterministic_validation": {
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+        "final_status": "accepted",
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+          "ATMOSPHERE",
+          "ATMOSPHERIC WINDS",
+          "UPPER LEVEL WINDS"
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+        "reason_for_stopping": "The article concerns stratospheric polar vortex dynamics, which supports the parent concept of upper-level winds, but it does not specifically report a supplied child variable such as wind speed, wind direction, U/V components, or tendencies. 'Strong' vortex could imply wind speed, but this is not sufficiently explicit in the provided abstract.",
+        "review_required": false,
+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC WINDS",
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+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      },
+      {
+        "UUID": "858a80ff-5aa4-4590-b2e2-e88a802a6ee4",
+        "branch_id": "topic:topic_0002/term:term_0011/variable:variable_0004/variable:variable_0010",
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+        "classifier_evidence": "The article focuses on the exceptionally strong and persistent Arctic stratospheric polar vortex and investigates its dynamical variation; a polar vortex is a large-scale rotating circulation directly tied to atmospheric vorticity.",
+        "confidence": {
+          "final": 0.74,
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+          "ATMOSPHERE",
+          "ATMOSPHERIC WINDS",
+          "WIND DYNAMICS",
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+        "reason_for_stopping": "The article discusses the Arctic stratospheric polar vortex and its dynamical persistence, but does not specifically mention or provide evidence for potential vorticity or vorticity advection as variables.",
+        "review_required": true,
+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC WINDS",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+          {
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+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
+            "stage": "review_flagging"
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+        ]
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+    "processing_metadata": {
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+      "application_version": "0.1.0",
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+        "temperature": 0.0,
+        "timeout_seconds": 30.0
+      },
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+      "processed_at": "2026-07-11T21:23:36.221910+00:00",
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+    },
+    "processing_status": "completed",
+    "review_status": "not_required",
+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/dd0221cea8e3fedffec2f981eb78bffbb7b4eb7e14be9b0575f487e3a4e12cff.json

@@ -0,0 +1,321 @@
+{
+  "cache_key": "dd0221cea8e3fedffec2f981eb78bffbb7b4eb7e14be9b0575f487e3a4e12cff",
+  "identity": {
+    "DOI": "10.1016/J.AEAOA.2025.100324",
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+    "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
+  },
+  "result": {
+    "Abstract": "An upsurge in the pollution level areas with a high concentration of brick and cement factories Nepal is concerning. Nitrogen dioxide (NO 2 ), key air quality indicator, can be effectively monitored from space. This study utilizes high-resolution satellite observations NO TROPOspheric Monitoring Instrument (TROPOMI). It examines distribution over emerging sources nitrogen oxides 2018 to 2021. Rapid growth has turned Lumbini-Butwal-Palpa corridor, Midwest Nepal, more polluted than capital city Kathmandu. Between 2019 2021, levels this corridor rose considerably, while it remained steady Kathmandu Valley. TROPOMI-derived inferred x emissions nearly doubled span three years. Conversely, Valley exhibited no significant changes except 2020 when declined. drop coincided COVID-19-related travel restrictions other reduced activities. recorded by Ozone (OMI) 2005 shows an annual increase 3.5 % both regions. A comparison between EDGAR inventory estimates for reveal comparable values but around 35 higher discrepancy city, as well rapid rise due large-scale development industries, highlights need timely updates bottom-up emission inventory.",
+    "DOI": "10.1016/J.AEAOA.2025.100324",
+    "Title": "Cement and brick factories contribute elevated levels of NO2 pollution in Nepal: Evidence of high-resolution view from space",
+    "Year": 2025,
+    "classification_outcome": "classified",
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+        "branch_id": "topic:topic_0002/term:term_0002/variable:variable_0002",
+        "canonical_path": "ATMOSPHERE > AIR QUALITY > EMISSIONS",
+        "classifier_evidence": "Abstract reports TROPOMI-derived inferred NOx emissions nearly doubled and compares results with EDGAR emission inventory estimates for industrial regions.",
+        "confidence": {
+          "final": 0.86,
+          "term": null,
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+        "final_status": "accepted",
+        "level": "Variable_Level_1",
+        "name": "EMISSIONS",
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+        "parent_uuid": "77397026-09c9-44e0-b85f-77b2bc9b1630",
+        "path_components": [
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+          "EMISSIONS"
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "AIR QUALITY",
+        "topic": "ATMOSPHERE",
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+          {
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      {
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+        "branch_id": "topic:topic_0002/term:term_0002/variable:variable_0004",
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+        "classifier_evidence": "Title and abstract focus on elevated NO2 pollution and nitrogen oxides from satellite observations, including distribution over sources of nitrogen oxides.",
+        "confidence": {
+          "final": 0.98,
+          "term": null,
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+          "variable_level_1": 0.98,
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+          "valid": true,
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+        "final_status": "accepted",
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+        "original_candidate": null,
+        "parent_uuid": "77397026-09c9-44e0-b85f-77b2bc9b1630",
+        "path_components": [
+          "ATMOSPHERE",
+          "AIR QUALITY",
+          "NITROGEN OXIDES"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "AIR QUALITY",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0004"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
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+        ]
+      },
+      {
+        "UUID": "f8e65155-27c1-483e-a9b8-85399897c3ae",
+        "branch_id": "topic:topic_0002/term:term_0005/variable:variable_0004/variable:variable_0009",
+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC CHEMISTRY > NITROGEN COMPOUNDS > NITROGEN DIOXIDE",
+        "classifier_evidence": "Title and abstract explicitly state elevated nitrogen dioxide (NO2) pollution and monitoring of NO2 from space.",
+        "confidence": {
+          "final": 0.96,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": 0.96,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "NITROGEN DIOXIDE",
+        "original_candidate": null,
+        "parent_uuid": "9e5ec924-2fd3-4cbb-a7eb-ffde114d0cb9",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC CHEMISTRY",
+          "NITROGEN COMPOUNDS",
+          "NITROGEN DIOXIDE"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC CHEMISTRY",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "e82ebd1c-8241-4ca0-95a9-a6e1432519cd",
+        "branch_id": "topic:topic_0002/term:term_0005/variable:variable_0004/variable:variable_0010",
+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC CHEMISTRY > NITROGEN COMPOUNDS > NITROGEN OXIDES",
+        "classifier_evidence": "Abstract says the study examines distribution over emerging sources of nitrogen oxides and reports TROPOMI-derived inferred NOx emissions.",
+        "confidence": {
+          "final": 0.78,
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+          "variable_level_1": null,
+          "variable_level_2": 0.78,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "NITROGEN OXIDES",
+        "original_candidate": null,
+        "parent_uuid": "9e5ec924-2fd3-4cbb-a7eb-ffde114d0cb9",
+        "path_components": [
+          "ATMOSPHERE",
+          "ATMOSPHERIC CHEMISTRY",
+          "NITROGEN COMPOUNDS",
+          "NITROGEN OXIDES"
+        ],
+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC CHEMISTRY",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
+              "candidate_id": "variable_0010"
+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
+        "UUID": "4f45247d-69e5-47fe-9dd4-2a28d42462cd",
+        "branch_id": "topic:topic_0012/term:term_0003",
+        "canonical_path": "SPECTRAL/ENGINEERING > HYPERSPECTRAL IMAGERY",
+        "classifier_evidence": "The study uses high-resolution satellite observations from TROPOMI and OMI to retrieve tropospheric NO2; these satellite spectrometer observations are based on high-spectral-resolution measurements used for atmospheric trace-gas mapping.",
+        "confidence": {
+          "final": 0.72,
+          "term": 0.72,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": null,
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+          "valid": true,
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+        "final_status": "accepted",
+        "level": "Term",
+        "name": "HYPERSPECTRAL IMAGERY",
+        "original_candidate": null,
+        "parent_uuid": "83150c54-5da8-4ee8-9579-19b95a8dc10c",
+        "path_components": [
+          "SPECTRAL/ENGINEERING",
+          "HYPERSPECTRAL IMAGERY"
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+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": true,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "inferred",
+        "term": "HYPERSPECTRAL IMAGERY",
+        "topic": "SPECTRAL/ENGINEERING",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          },
+          {
+            "DOI": null,
+            "code": "REVIEW_RECOMMENDED_WEAK_SUPPORT",
+            "details": {
+              "confidence_final": 0.72,
+              "level": "Term",
+              "support_type": "inferred"
+            },
+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
+            "stage": "review_flagging"
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+        ]
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+    "processing_metadata": {
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+        "topic": "topic-v1",
+        "variable": "variable-v1"
+      },
+      "run_id": "manual-small-run",
+      "started_at": "2026-07-11T21:22:00.241279+00:00",
+      "title_available": true,
+      "vocabulary_hash": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad",
+      "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
+    },
+    "processing_status": "completed",
+    "review_status": "not_required",
+    "warnings": []
+  }
+}

outputs/small_run_gpt5.5_50/cache/e33341f62ce2f181cf0f1cc1a60f5147e257002e4091be03763fba1fb10fef98.json

@@ -0,0 +1,785 @@
+{
+  "cache_key": "e33341f62ce2f181cf0f1cc1a60f5147e257002e4091be03763fba1fb10fef98",
+  "identity": {
+    "DOI": "10.1016/J.ENVRES.2026.124375",
+    "application_version": "0.1.0",
+    "article_fingerprint": "8fef3d7b33f7f33832fbce5f09cbe17c650c88aad8b4a17bfeacc69fd66d6622",
+    "configuration_hash": "92d5193323468144f8da5f7c8629eb8dca34a0b1a1b0d4b74b09ee2b478c90fc",
+    "model_max_retries": 2,
+    "model_name": "gpt-5.5",
+    "model_provider": "openai",
+    "model_temperature": 0.0,
+    "model_timeout_seconds": 30.0,
+    "prompt_version_term": "term-v1",
+    "prompt_version_topic": "topic-v1",
+    "prompt_version_variable": "variable-v1",
+    "vocabulary_version": "48dd0333ef10aff879fcd96dbe885a2bb20029b5c968ca7f200259ce45be58ad"
+  },
+  "result": {
+    "Abstract": "Rivers transport and process carbon along the landocean continuum and emit CH4 to the atmosphere. Widespread human perturbation of river networks has disrupted the natural flow regime, regulating the supply of allochthonous matter and subsequently affecting the in-stream biogeochemical processes. The spatiotemporal variability of CH4 in human-impacted rivers remains less explored, especially in the highly populated regions of South Asia, resulting in uncertainties in the global flux estimates. This study investigates the evolution of CH4 in tropical rivers and engineered canals along a gradient of increasing anthropogenic disturbances. Sampling was conducted along different sections (selected based on the level of human perturbations) of the Sabarmati River, Mahi River, and Narmada Canal, located in western India, during high and low flow conditions. It was observed that water stagnancy resulted in higher dissolved CH4 in the lined and stagnant Sabarmati riverfront (a discontinuity in the free-flowing river continuum) compared to the lined and flowing Narmada Canal. Furthermore, the release of wastewater resulted in a 70 km low-oxygen environment accompanied by extremely high CH4 concentrations in the downstream section of the Sabarmati. CH4 exhibited positive correlations with temperature, dissolved inorganic nitrogen, and particulate organic carbon. 13CCH4 indicated that acetoclastic methanogenesis was the major pathway of CH4 production, and evidence of CH4 oxidation was observed in the stagnant riverfront of the Sabarmati. This study provides estimates of CH4 fluxes from the region and highlights the climate implications of river development projects, along with the dominant role of wastewater in rivers with reduced natural flow.",
+    "DOI": "10.1016/J.ENVRES.2026.124375",
+    "Title": "Shifting flow regimes and water quality regulate CH4 dynamics in anthropogenically perturbed tropical rivers and canals",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
+      {
+        "UUID": "1baa552d-c563-43fb-b618-54651f8b07e6",
+        "branch_id": "topic:topic_0014/term:term_0006/variable:variable_0001",
+        "canonical_path": "TERRESTRIAL HYDROSPHERE > SURFACE WATER > SURFACE WATER CHEMISTRY",
+        "classifier_evidence": "The study measures and analyzes surface-water chemical/water-quality variables including dissolved CH4, low oxygen conditions, dissolved inorganic nitrogen, particulate organic carbon, 13C-CH4, wastewater effects, and CH4 oxidation/production in rivers and canals.",
+        "confidence": {
+          "final": 0.95,
+          "term": null,
+          "topic": null,
+          "variable_level_1": 0.95,
+          "variable_level_2": null,
+          "variable_level_3": null
+        },
+        "deterministic_validation": {
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+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_1",
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+        "classifier_evidence": "CH4 exhibited positive correlations with temperature during the river and canal sampling study.",
+        "confidence": {
+          "final": 0.75,
+          "term": null,
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "WATER QUALITY/WATER CHEMISTRY",
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+        "classifier_evidence": "The abstract states that rivers emit CH4 to the atmosphere and that the study provides estimates of CH4 fluxes from tropical rivers and canals, highlighting climate implications.",
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+          "ATMOSPHERE/SURFACE INTERACTIONS",
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+        "support_type": "explicit",
+        "term": "ATMOSPHERE/SURFACE INTERACTIONS",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
+            "DOI": null,
+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+}

outputs/small_run_gpt5.5_50/cache/eb444eb18eacb4b9e366abc1df39939b9f54128342cadc5b1c32d03800df507c.json

@@ -0,0 +1,582 @@
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+  "result": {
+    "Abstract": "Nuanced characterizations of moisture source dynamics and local hydrometeorological processes are essential for interpreting long-term records of stable isotopes in precipitation. Here, we analyze over two decades of stable isotope records from a site in the Great Plains of United States, revealing a distinct seasonal contrast in 18O variability between warm (MarchNovember) and cold (DecemberFebruary) periods. During the warm season, isotopic enrichment was largely driven by enhanced convective activity and sub-cloud evaporation under high VPD conditions. Back-trajectory diagnostics indicate that continental moisture sources dominate precipitation at the study site, while Gulf of Mexico transport via the Great Plains low-level jet exerts a disproportionate influence on 18O and d-excess variability. During extreme precipitation years, isotopic signatures reflect the combined effects of atmospheric circulation anomalies and local aridity. The 2012 drought year exhibited elevated 18O and reduced d-excess consistent with enhanced kinetic fractionation under dry conditions, whereas the wet year 2019 showed isotopic enrichment associated with intensified Gulf-sourced moisture transport under humid conditions. These findings demonstrate how precipitation 18O in the Great Plains integrates both local evaporative demand and large-scale moisture transport processes. Given ongoing challenges in representing humidity trends and regional hydroclimate dynamics in climate models, improved characterization of moisture sources and isotope variability is critical for evaluating model projections and interpreting long-term climate change in semi-arid continental regions.",
+    "DOI": "10.1016/J.SCITOTENV.2026.181790",
+    "Title": "Local and distal processes determine precipitation isotope records in the Great Plains USA",
+    "Year": 2026,
+    "classification_outcome": "classified",
+    "classifications": [
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+        "UUID": "1532e590-a62d-46e3-8d03-2351bc48166a",
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+        "canonical_path": "ATMOSPHERE > PRECIPITATION",
+        "classifier_evidence": "The study analyzes stable isotope records in precipitation, seasonal precipitation isotope variability, and extreme precipitation years in the Great Plains.",
+        "confidence": {
+          "final": 0.95,
+          "term": 0.95,
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+          "variable_level_1": null,
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+          "variable_level_3": null
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+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Term",
+        "name": "PRECIPITATION",
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+        "parent_uuid": "c47f6052-634e-40ef-a5ac-13f69f6f4c2a",
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+        "reason_for_stopping": "The article is primarily about stable isotope composition/variability in precipitation, moisture sources, and hydrometeorological controls. None of the supplied direct-child candidates represents precipitation isotopes, so the supported concept cannot be descended reliably beyond PRECIPITATION.",
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+        "review_status": "not_required",
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+        "support_type": "explicit",
+        "term": "PRECIPITATION",
+        "topic": "ATMOSPHERE",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      {
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+        "deterministic_validation": {
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+        "term": "ATMOSPHERIC WATER VAPOR",
+        "topic": "ATMOSPHERE",
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+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      {
+        "UUID": "427e5121-a142-41cb-a8e9-a70b7f98eb6a",
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+        "classifier_evidence": "The abstract discusses high VPD conditions, humid/dry conditions, local aridity, evaporative demand, and challenges representing humidity trends.",
+        "confidence": {
+          "final": 0.72,
+          "term": null,
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+          "variable_level_2": 0.72,
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+        },
+        "deterministic_validation": {
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+          "valid": true,
+          "warnings": []
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+        "final_status": "accepted",
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+          "ATMOSPHERIC WATER VAPOR",
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+        "review_status": "not_required",
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+        "support_type": "mixed",
+        "term": "ATMOSPHERIC WATER VAPOR",
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
+            "details": {
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+            },
+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
+            "stage": "deterministic_validation"
+          }
+        ]
+      },
+      {
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+        "canonical_path": "ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROCESSES > EVAPORATION",
+        "classifier_evidence": "Abstract explicitly states isotopic enrichment was driven by \"sub-cloud evaporation\" and discusses \"local evaporative demand\" under dry/high VPD conditions.",
+        "confidence": {
+          "final": 0.93,
+          "term": null,
+          "topic": null,
+          "variable_level_1": null,
+          "variable_level_2": 0.93,
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+        },
+        "deterministic_validation": {
+          "errors": [],
+          "valid": true,
+          "warnings": []
+        },
+        "errors": [],
+        "final_status": "accepted",
+        "level": "Variable_Level_2",
+        "name": "EVAPORATION",
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+        "parent_uuid": "3c4fe00c-6fb1-403e-a053-3a0174a6dfe6",
+        "path_components": [
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+          "ATMOSPHERIC WATER VAPOR",
+          "WATER VAPOR PROCESSES",
+          "EVAPORATION"
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+        "reason_for_stopping": "No direct Variable children are available.",
+        "review_required": false,
+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "ATMOSPHERIC WATER VAPOR",
+        "topic": "ATMOSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "index": null,
+            "message": "Application-supplied candidate ID was retained as provenance metadata.",
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+      },
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+        "parent_uuid": "3c4fe00c-6fb1-403e-a053-3a0174a6dfe6",
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+        "review_status": "not_required",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC WATER VAPOR",
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+            "index": null,
+            "message": "Manual scientific review is recommended because support is weak or inferred.",
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+        "branch_id": "topic:topic_0002/term:term_0011/variable:variable_0003/variable:variable_0001",
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+        "classifier_evidence": "The abstract specifically discusses Gulf of Mexico moisture transport via the Great Plains low-level jet, a low-level/boundary-layer wind feature influencing isotope variability.",
+        "confidence": {
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+        "final_status": "accepted",
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+        "support_type": "inferred",
+        "term": "ATMOSPHERIC WINDS",
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+          }
+        ]
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+        "classifier_evidence": "Title and abstract repeatedly state analysis of stable isotope records in precipitation, including 18O variability and d-excess.",
+        "confidence": {
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+        "reason_for_stopping": "No direct Variable children are available.",
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+        "review_status": "not_required",
+        "semantic_validation": null,
+        "support_type": "explicit",
+        "term": "WATER QUALITY/WATER CHEMISTRY",
+        "topic": "TERRESTRIAL HYDROSPHERE",
+        "warnings": [
+          {
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+            "code": "CANDIDATE_ID_RETAINED_AS_PROVENANCE",
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+        ]
+      },
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outputs/small_run_gpt5.5_50/cache/ee09adb869b6c77d67b801851ffb501550e5168ae5a898b0316ed201754c8117.json

@@ -0,0 +1,559 @@
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+    "Title": "Emission Control and Sensitivity Regime Shifts Drive the Decline in Extreme Ozone Concentration in the Sichuan Basin During 20152024",
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outputs/small_run_gpt5.5_50/cache/f1639ab3c4f07a2fc3ff3c140e6c00215e1faa5a9786ccff4f35f4d6354be81e.json

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+        "support_type": "explicit",
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+            "DOI": null,
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outputs/small_run_gpt5.5_50/cache/f32d820d3405a6a4c097e6fc06949104e68b58c1fe962983ad8cc327769ba658.json

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+    "Abstract": "Accurate intercalibration of passive microwave (PMW) radiometers is a prerequisite for generating consistent Fundamental Climate Data Records (FCDR). However, reconciling the radiometric baselines of new-generation sensors, particularly those in drifting orbits (e.g., FY-3G) or observing heterogeneous land surfaces, remains a challenge due to severe spatiotemporal sampling mismatches and complex scene-dependent biases. To address these issues, we propose a novel Periodicity and Environmental Constraints based Multi-stage Inter-Calibration (PEC-MIC) framework to harmonize the FY-3G MWRI-RM and FY-3F MWRI-II observations with the GPM/GMI reference standard. The framework employs a reconstruction-then-correction strategy comprising three stages. First, a physically constrained interpolation utilizes Fourier harmonic modeling to reconstruct the diurnal temperature cycle, effectively bridging the sampling gaps caused by orbital drift. Second, a global non-linear correction based on Locally Weighted Scatterplot Smoothing (LOWESS) removes the intensity-dependent systematic errors of the sensors. Third, an environmentally-weighted PCA refinement isolates and eliminates residual scene-dependent biases (e.g., surface roughness artifacts) over complex terrain. Validation results demonstrate that the PEC-MIC significantly outperforms linear methods. The spatiotemporal interpolation is verified to achieve high accuracy, yielding RMSE of approximately 1.2 K even in data-sparse regions. Following the intercalibration process, the root mean square difference (RMSD) between the FY-3 sensors and the GMI is reduced by 50%70%, stabilizing within 0.81.5 K for most channels over land. The proposed method successfully decouples sensor-level drifts from geophysical variability, providing a robust solution for harmonizing diverse satellite constellations.",
+    "DOI": "10.1016/J.RSE.2026.115429",
+    "Title": "Intercalibration of the newest Microwave Radiation Imagers onboard the Chinese Fengyun-3 satellites",
+    "Year": 2026,
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+        "canonical_path": "SPECTRAL/ENGINEERING > MICROWAVE > BRIGHTNESS TEMPERATURE",
+        "classifier_evidence": "The study intercalibrates passive microwave radiometer observations from FY-3 MWRI instruments against GPM/GMI, reporting channel differences in Kelvin, which is characteristic of microwave brightness temperature products/FCDRs.",
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+        "term": "MICROWAVE",
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+        "branch_id": "topic:topic_0012/term:term_0012",
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+        "classifier_evidence": "The work addresses radiometric baselines, sensor-level drifts, intensity-dependent systematic errors, and scene-dependent biases of FY-3G MWRI-RM and FY-3F MWRI-II sensors.",
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+        "reason_for_stopping": "The article supports the broad parent concept of sensor characteristics through intercalibration of passive microwave radiometers, radiometric baselines, sensor biases, and sensor-level drifts, but it does not directly discuss any supplied child characteristic such as scan angle/IFOV viewing geometry, geolocation, temperatures, pressure, electrical, phase/amplitude, or impulse response.",
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+        "term": "SENSOR CHARACTERISTICS",
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outputs/small_run_gpt5.5_50/cache/f67f4df55699f0133cf66d0af61227307b9628910810b0ec45230e8e66c7572f.json

@@ -0,0 +1,689 @@
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+  "result": {
+    "Abstract": "Coastal water color, as captured by the hue angle from satellite imagery, provides a comprehensive indicator of ecosystem dynamics influenced environmental and human factors. This study optimizes bias-correction model for deriving MODIS data using large in situ hyperspectral dataset, achieving improved accuracy ( R 2 = 0.71, MAPE 5.67%, RMSE 12.05). Applied to Qinhuangdao coastal waters over 20032024, uncovers nearshore-to-offshore gradient, V-shaped seasonal pattern, triphasic interannual trend: degradation (20032011), recovery (20122023), 2024 reversal. Phytoplankton chlorophyll-a (Chl-a) emerges dominant driver color variability 0.67), with dissolved inorganic nitrogen (DIN) key influence 0.54), underscoring nutrient-driven eutrophication. Policy measures, including 2008 Olympic pollution controls 2018 Bohai Sea remediation, correlate post-2012 improvements. The demonstrates greater sensitivity than Forel-Ule Index, offering valuable tool management.",
+    "DOI": "10.1016/J.RSMA.2026.104944",
+    "Title": "Policy-driven water quality trends in Qinhuangdao coastal waters, China (20032024): Nutrient controls and climate vulnerabilities via high-accuracy hue angle",
+    "Year": 2026,
+    "classification_outcome": "classified",
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+        "branch_id": "topic:topic_0009/term:term_0025/variable:variable_0001",
+        "canonical_path": "OCEANS > WATER QUALITY > ALGAL BLOOMS",
+        "classifier_evidence": "Abstract identifies phytoplankton chlorophyll-a as the dominant driver of coastal water color variability and describes DIN-driven eutrophication, which supports algal biomass/bloom-related water quality impacts.",
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outputs/small_run_gpt5.5_50/cache/fdad5c592748e4e7307f471f12b59ce8196462af5429213fafaf19a9f36f34cc.json

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+    "Abstract": "Abstract Effective drought management is critical for agriculturebased economies like India. This study examines whether a transformerbased architecture can function as a robust pretrained backbone for operational drought forecasting across India. We utilized a pretrained transformer model, which was trained on the Indian Monsoon Data Assimilation and Analysis (IMDAA) precipitation data set (source domain) from 1979 to 2014. Transfer learning capabilities were evaluated by applying the pretrained model without targetdomain adaptation to 11 precipitation products (target data set) differing in source and spatial resolution. The target data sets were compared with the source domain to examine how well they reproduce regional climatic patterns. Transfer learning performance was evaluated over two distinct periods: the overall study period (20012019), partially overlapping with training, and the unseen testing period (20172019). Spatial evaluation is performed across six climatic zones delineated based on the KoppenGeiger classification. We utilized the 3month Standardized Precipitation Evapotranspiration Index (SPEI3) to quantify meteorological drought at a seasonal scale. The pretrained model maintained stable performance across data sets and climate zones, with correlations exceeding 0.7 during winter and premonsoon seasons but declined during the monsoon. Transfer learning evaluation revealed consistent performance across most data sets, with RMSE and MAE within 0.05 and NSE within 0.1 compared to the source domain. The composite skill score rankings showed that ERA5 and MSWEP were the closest to the IMDAA benchmark during the unseen period. The variation in performance across data sets underscores the importance of data setspecific finetuning for operational reliability. , Plain Language Summary Developing a drought forecasting model that works across different resolutions and data sources is essential for datascarce regions like India. We evaluated the transfer learning capabilities of a transformer based pretrained model across India. The transfer learning performance was evaluated across two time periods (20012019 and 20172019) and six climate zones in India. The pretrained model was evaluated across 11 different precipitation data sets from various sources and spatial resolutions. Our results showed that the model performed stable across the majority of data sets with small error margins. Performance across the ERA5 and MSWEP data sets aligned best with our source domain performance. These results highlight that transformers can generalize across resolutions and data sources. However, the study highlights that targeted finetuning remains important for ensuring reliable and longterm operational predictions. , Key Points Most data sets showed RMSE and MAE within 0.05 and NSE within 0.1, proving stable transferability The forecasting skill across target data sets peaked during winter and premonsoon seasons (correlations > 0.7) but declined during the highvariability monsoon ERA5 and MSWEP showed strong transferability while matching the forecasting performance across the source domain",
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