Machine Relations Index mri_score_v2.1+2026-10-29+225b998b091e

CITATION.cff

@@ -5,8 +5,9 @@ title: Machine Relations Index
 authors:
   - family-names: Parrott
     given-names: Jaxon
-version: "mri_score_v2.1+2026-10-27+06e001ee9f9a"
-date-released: "2026-10-27"
+    orcid: "https://orcid.org/0009-0004-0039-3913"
+version: "mri_score_v2.1+2026-10-29+225b998b091e"
+date-released: "2026-10-29"
 license: CC-BY-4.0
 url: "https://machinerelations.ai/index"
 identifiers:
@@ -25,8 +26,9 @@ preferred-citation:
   authors:
     - family-names: Parrott
       given-names: Jaxon
+      orcid: "https://orcid.org/0009-0004-0039-3913"
   year: 2026
-  version: "mri_score_v2.1+2026-10-27+06e001ee9f9a"
+  version: "mri_score_v2.1+2026-10-29+225b998b091e"
   url: "https://machinerelations.ai/index"
   doi: "10.5281/zenodo.23652423"
   notes: "Machine Relations Research. CC BY 4.0. Machine Relations was coined by Jaxon Parrott in 2024."

README.md

@@ -39,9 +39,9 @@ Machine Relations was coined by Jaxon Parrott in 2024.
 
 Keywords: AI search, generative engine optimization, answer engine citations, Machine Relations.
 
-Weekly public snapshot of the Machine Relations Index. Version `mri_score_v2.1+2026-10-27+06e001ee9f9a`, generated 2026-10-27, window 2026-05-10 to 2026-10-27. Methodology `mri_score_v2.1`. 34172 cited source domains, 157 segments, 6 answer engines. Evidence floor: 10 observed runs and 7 distinct run dates.
+Weekly public snapshot of the Machine Relations Index. Version `mri_score_v2.1+2026-10-29+225b998b091e`, generated 2026-10-29, window 2026-05-10 to 2026-10-29. Methodology `mri_score_v2.1`. 34602 cited source domains, 157 segments, 6 answer engines. Evidence floor: 10 observed runs and 7 distinct run dates.
 
-`machine-relations-index.json` is the public artifact copied byte for byte. Its sha256 is `9964b4936fa298524514713c0f1826a7bfa4cc23c987a42549045bdd39eca3dd` and matches `mri-release-manifest.json`. The CSV files are flat views of that artifact for the dataset viewer. They are not a second measurement.
+`machine-relations-index.json` is the public artifact copied byte for byte. Its sha256 is `490779bb74b49cac80f0e2826688016f518468f99036021c2b2bb69da534c323` and matches `mri-release-manifest.json`. The CSV files are flat views of that artifact for the dataset viewer. They are not a second measurement.
 
 ## Citation
 
@@ -55,7 +55,7 @@ Hugging Face dataset: https://huggingface.co/datasets/machine-relations/machine-
   author = {Parrott, Jaxon},
   title = {Machine Relations Index},
   year = {2026},
-  version = {mri_score_v2.1+2026-10-27+06e001ee9f9a},
+  version = {mri_score_v2.1+2026-10-29+225b998b091e},
   publisher = {Machine Relations Research},
   url = {https://machinerelations.ai/index},
   doi = {10.5281/zenodo.23652423},

engines.csv

@@ -1,7 +1,7 @@
 engine,requested_model,resolved_model,status,days_observed,runs_total,runs_observed,last_success_date,last_observed_date
-chatgpt,gpt-4.1-mini,gpt-4.1-mini-2025-04-14,healthy,160,7400,6583,2026-10-27,2026-10-27
-claude,claude-haiku-4-5,claude-haiku-4-5-20251001,healthy,163,7433,5977,2026-10-27,2026-10-27
-gemini,gemini-flash-latest,gemini-3.8-flash,degraded,142,7206,2237,2026-10-27,2026-10-27
-google_ai_mode,,,healthy,121,2121,1639,2026-10-27,2026-10-27
-google_ai_overviews,,,healthy,104,1462,976,2026-10-27,2026-10-27
-perplexity,sonar,sonar,healthy,164,7513,7512,2026-10-27,2026-10-27
+chatgpt,gpt-4.1-mini,gpt-4.1-mini-2025-04-14,healthy,162,7656,6812,2026-10-29,2026-10-29
+claude,claude-haiku-4-5,claude-haiku-4-5-20251001,healthy,165,7692,6169,2026-10-29,2026-10-29
+gemini,gemini-flash-latest,gemini-3.8-flash,degraded,143,7348,2248,2026-10-29,2026-10-28
+google_ai_mode,,,healthy,123,2157,1662,2026-10-29,2026-10-29
+google_ai_overviews,,,healthy,106,1480,990,2026-10-29,2026-10-29
+perplexity,sonar,sonar,healthy,166,7772,7771,2026-10-29,2026-10-29

machine-relations-index.json

@@ -1,3 +1,3 @@
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-size 69325660
+oid sha256:490779bb74b49cac80f0e2826688016f518468f99036021c2b2bb69da534c323
+size 70473770

mri-release-manifest.json

@@ -6,29 +6,29 @@
   },
   "publisher": "Machine Relations Research",
   "license": "https://creativecommons.org/licenses/by/4.0/",
-  "citation": "Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai. Release mri_score_v2.1+2026-10-27+06e001ee9f9a. https://machinerelations.ai/index. CC BY 4.0.",
+  "citation": "Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai. Release mri_score_v2.1+2026-10-29+225b998b091e. https://machinerelations.ai/index. CC BY 4.0.",
   "url": "https://machinerelations.ai/index",
-  "release_id": "mri_score_v2.1+2026-10-27+06e001ee9f9a",
+  "release_id": "mri_score_v2.1+2026-10-29+225b998b091e",
   "manifest_schema": "mri_release_manifest_v1",
   "methodology_version": "mri_score_v2.1",
   "contract_version": "machine_relations_index_public_view_v2.0",
   "artifact": {
     "path": "/data/machine-relations-index.json",
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-    "bytes": 69325660,
-    "generated": "2026-10-27",
+    "sha256": "490779bb74b49cac80f0e2826688016f518468f99036021c2b2bb69da534c323",
+    "bytes": 70473770,
+    "generated": "2026-10-29",
     "window": {
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+      "data_through": "2026-10-29",
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@@ -37,73 +37,73 @@
       "requested_model": "gpt-4.1-mini",
       "resolved_model": "gpt-4.1-mini-2025-04-14",
       "status": "healthy",
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-      "last_observed_date": "2026-10-27",
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-      "runs_total": 7400,
-      "runs_observed": 6583
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+      "last_observed_date": "2026-10-29",
+      "days_observed": 162,
+      "runs_total": 7656,
+      "runs_observed": 6812
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     {
       "engine": "claude",
       "requested_model": "claude-haiku-4-5",
       "resolved_model": "claude-haiku-4-5-20251001",
       "status": "healthy",
-      "last_success_date": "2026-10-27",
-      "last_observed_date": "2026-10-27",
-      "days_observed": 163,
-      "runs_total": 7433,
-      "runs_observed": 5977
+      "last_success_date": "2026-10-29",
+      "last_observed_date": "2026-10-29",
+      "days_observed": 165,
+      "runs_total": 7692,
+      "runs_observed": 6169
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     {
       "engine": "gemini",
       "requested_model": "gemini-flash-latest",
       "resolved_model": "gemini-3.8-flash",
       "status": "degraded",
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-      "last_observed_date": "2026-10-27",
-      "days_observed": 142,
-      "runs_total": 7206,
-      "runs_observed": 2237
+      "last_success_date": "2026-10-29",
+      "last_observed_date": "2026-10-28",
+      "days_observed": 143,
+      "runs_total": 7348,
+      "runs_observed": 2248
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       "engine": "google_ai_mode",
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       "resolved_model": null,
       "status": "healthy",
-      "last_success_date": "2026-10-27",
-      "last_observed_date": "2026-10-27",
-      "days_observed": 121,
-      "runs_total": 2121,
-      "runs_observed": 1639
+      "last_success_date": "2026-10-29",
+      "last_observed_date": "2026-10-29",
+      "days_observed": 123,
+      "runs_total": 2157,
+      "runs_observed": 1662
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     {
       "engine": "google_ai_overviews",
       "requested_model": null,
       "resolved_model": null,
       "status": "healthy",
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-      "last_observed_date": "2026-10-27",
-      "days_observed": 104,
-      "runs_total": 1462,
-      "runs_observed": 976
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+      "last_observed_date": "2026-10-29",
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     {
       "engine": "perplexity",
       "requested_model": "sonar",
       "resolved_model": "sonar",
       "status": "healthy",
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-      "last_observed_date": "2026-10-27",
-      "days_observed": 164,
-      "runs_total": 7513,
-      "runs_observed": 7512
+      "last_success_date": "2026-10-29",
+      "last_observed_date": "2026-10-29",
+      "days_observed": 166,
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     }
   ],
   "coverage": {
     "required_engines": 6,
     "healthy_engines": 5,
     "degraded": true,
-    "statement": "Degraded coverage: 5 of 6 answer engines are currently returning citation-bearing answers. gemini quiet (20 of 125 collected calls produced an observation, 16%). Coverage across the window is uneven: gemini returned citation-bearing answers on 142 of 164 observed days (2,237 of 7,206 returned runs produced a citation); google_ai_mode returned citation-bearing answers on 121 of 164 observed days (1,639 of 2,121 returned runs produced a citation); google_ai_overviews returned citation-bearing answers on 104 of 164 observed days (976 of 1,462 returned runs produced a citation). Engines below 90% of the window are named here; per-engine day and run counts for all 6 are in engine_roster."
+    "statement": "Degraded coverage: 5 of 6 answer engines are currently returning citation-bearing answers. gemini silent (0 of 10 collected calls produced an observation). Coverage across the window is uneven: gemini returned citation-bearing answers on 143 of 166 observed days (2,248 of 7,348 returned runs produced a citation); google_ai_mode returned citation-bearing answers on 123 of 166 observed days (1,662 of 2,157 returned runs produced a citation); google_ai_overviews returned citation-bearing answers on 106 of 166 observed days (990 of 1,480 returned runs produced a citation). Engines below 90% of the window are named here; per-engine day and run counts for all 6 are in engine_roster."
   },
   "supersession": {
     "previous_version": "mri_score_v1.1_6_engine",

release.json

@@ -1,10 +1,10 @@
 {
-  "release_id": "mri_score_v2.1+2026-10-27+06e001ee9f9a",
-  "generated": "2026-10-27",
-  "data_through": "2026-10-27",
+  "release_id": "mri_score_v2.1+2026-10-29+225b998b091e",
+  "generated": "2026-10-29",
+  "data_through": "2026-10-29",
   "methodology_version": "mri_score_v2.1",
-  "artifact_sha256": "9964b4936fa298524514713c0f1826a7bfa4cc23c987a42549045bdd39eca3dd",
-  "domain_count": 34172,
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+  "domain_count": 34602,
   "segment_count": 157,
   "engine_count": 6,
   "concept_doi": "10.5281/zenodo.23652423"

segments.csv

@@ -29,31 +29,31 @@ consumer-health,is_x_worth,published,false,,272,302,12
 consumer-health,problem_first,published,false,,272,285,12
 consumer-health,top_list,published,false,,273,512,12
 consumer-health,x_vs_y,published,false,,274,292,12
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-consumer-products,how_choose,published,false,,255,618,11
-consumer-products,is_x_worth,published,false,,253,331,11
-consumer-products,problem_first,published,false,,250,337,11
-consumer-products,top_list,published,false,,247,544,11
-consumer-products,x_vs_y,published,false,,246,364,11
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+consumer-products,how_choose,published,false,,285,650,12
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+consumer-products,problem_first,published,false,,279,347,12
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 cybersecurity,best_x,published,false,,216,298,12
-cybersecurity,how_choose,published,false,,248,422,11
-cybersecurity,is_x_worth,published,false,,249,306,11
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+cybersecurity,is_x_worth,published,false,,278,323,12
 cybersecurity,news_topic,published,false,,599,1338,54
-cybersecurity,problem_first,published,false,,234,397,11
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-deep-tech-hardware,best_x,published,false,,230,507,11
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-deep-tech-hardware,problem_first,published,false,,213,285,11
-deep-tech-hardware,top_list,published,false,,215,393,11
-deep-tech-hardware,x_vs_y,published,false,,216,251,11
-education-training,best_x,published,false,,216,370,11
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-education-training,x_vs_y,published,false,,206,194,11
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 emergent-prosumer,best_x,published,false,,202,404,11
 emergent-prosumer,how_choose,published,false,,201,332,11
 emergent-prosumer,is_x_worth,published,false,,207,243,11
@@ -124,34 +124,34 @@ logistics-freight,how_choose,published,false,,130,273,8
 logistics-freight,is_x_worth,published,false,,136,238,8
 logistics-freight,problem_first,published,false,,129,213,9
 logistics-freight,top_list,published,false,,137,228,8
-logistics-freight,x_vs_y,published,false,,107,177,7
+logistics-freight,x_vs_y,published,false,,136,190,8
 martech-advertising,best_x,published,false,,106,213,7
 martech-advertising,how_choose,published,false,,106,198,7
-martech-advertising,is_x_worth,published,false,,102,188,7
+martech-advertising,is_x_worth,published,false,,131,208,8
 martech-advertising,news_topic,published,false,,578,1243,53
-martech-advertising,problem_first,published,false,,101,225,7
+martech-advertising,problem_first,published,false,,131,256,8
 martech-advertising,top_list,published,false,,100,204,7
 martech-advertising,x_vs_y,published,false,,100,162,7
 physical-consumer,best_x,published,false,,98,204,7
 physical-consumer,how_choose,published,false,,99,165,7
 physical-consumer,is_x_worth,published,false,,98,151,7
-physical-consumer,problem_first,collecting,false,2026-10-13,90,137,6
-physical-consumer,top_list,collecting,false,2026-10-13,91,222,6
-physical-consumer,x_vs_y,collecting,false,2026-10-13,92,106,6
-professional-services,best_x,collecting,false,2026-10-13,75,105,5
-professional-services,how_choose,collecting,false,2026-10-13,75,133,5
-professional-services,is_x_worth,collecting,false,2026-10-14,75,137,5
-professional-services,problem_first,collecting,false,2026-10-14,70,140,4
-professional-services,top_list,collecting,false,2026-10-14,70,117,4
-professional-services,x_vs_y,collecting,false,2026-10-14,72,141,4
-sales-gtm-crm,best_x,collecting,false,2026-10-14,64,151,3
-sales-gtm-crm,how_choose,collecting,false,2026-10-14,65,125,3
+physical-consumer,problem_first,published,false,,94,138,7
+physical-consumer,top_list,published,false,,96,222,7
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+professional-services,top_list,collecting,false,2026-10-14,99,140,5
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+sales-gtm-crm,best_x,collecting,false,2026-10-14,87,167,4
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 sales-gtm-crm,is_x_worth,collecting,false,2026-10-14,65,132,3
 sales-gtm-crm,problem_first,collecting,false,2026-10-14,66,141,3
 sales-gtm-crm,top_list,collecting,false,2026-10-14,65,141,3
-sales-gtm-crm,x_vs_y,collecting,false,2026-10-15,65,117,4
-vc-private-equity,best_x,collecting,false,2026-10-15,62,103,4
-vc-private-equity,how_choose,collecting,false,2026-10-15,63,135,4
+sales-gtm-crm,x_vs_y,collecting,false,2026-10-15,89,139,6
+vc-private-equity,best_x,collecting,false,2026-10-15,85,125,6
+vc-private-equity,how_choose,collecting,false,2026-10-15,85,172,5
 vc-private-equity,is_x_worth,collecting,false,2026-10-21,27,69,2
 vc-private-equity,problem_first,collecting,false,2026-10-21,27,91,2
 vc-private-equity,top_list,collecting,false,2026-10-21,26,82,2

zenodo-metadata.json

@@ -1,11 +1,12 @@
 {
   "title": "Machine Relations Index",
   "upload_type": "dataset",
-  "description": "<p>Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai. Publisher: Machine Relations Research. License: <a href=\"https://creativecommons.org/licenses/by/4.0/\">CC BY 4.0</a>.</p><p>Machine Relations was coined by Jaxon Parrott in 2024.</p><p>The Machine Relations Index measures how often AI answer engines cite each source domain. This record is one weekly public snapshot. The version is the release id <code>mri_score_v2.1+2026-10-27+06e001ee9f9a</code>, generated 2026-10-27, covering 2026-05-10 through 2026-10-27. 34172 cited source domains, 157 segments, 6 answer engines. Methodology mri_score_v2.1. Evidence floor: 10 observed runs and 7 distinct run dates.</p><p>The file machine-relations-index.json is the public artifact. Its sha256 is 9964b4936fa298524514713c0f1826a7bfa4cc23c987a42549045bdd39eca3dd.</p><p><a href=\"https://machinerelations.ai/index\">Machine Relations Index</a>. <a href=\"https://machinerelations.ai/research/machine-relations-index-methodology\">Methodology</a>. <a href=\"https://machinerelations.ai/api/mri/v2\">API</a>. <a href=\"https://machinerelations.ai/mcp\">MCP</a>. <a href=\"https://machinerelations.ai/research/machine-relations-index-report\">Weekly Index Report</a>.</p>",
+  "description": "<p>Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai. Publisher: Machine Relations Research. License: <a href=\"https://creativecommons.org/licenses/by/4.0/\">CC BY 4.0</a>.</p><p>Machine Relations was coined by Jaxon Parrott in 2024.</p><p>The Machine Relations Index measures how often AI answer engines cite each source domain. This record is one weekly public snapshot. The version is the release id <code>mri_score_v2.1+2026-10-29+225b998b091e</code>, generated 2026-10-29, covering 2026-05-10 through 2026-10-29. 34602 cited source domains, 157 segments, 6 answer engines. Methodology mri_score_v2.1. Evidence floor: 10 observed runs and 7 distinct run dates.</p><p>The file machine-relations-index.json is the public artifact. Its sha256 is 490779bb74b49cac80f0e2826688016f518468f99036021c2b2bb69da534c323.</p><p><a href=\"https://machinerelations.ai/index\">Machine Relations Index</a>. <a href=\"https://machinerelations.ai/research/machine-relations-index-methodology\">Methodology</a>. <a href=\"https://machinerelations.ai/api/mri/v2\">API</a>. <a href=\"https://machinerelations.ai/mcp\">MCP</a>. <a href=\"https://machinerelations.ai/research/machine-relations-index-report\">Weekly Index Report</a>.</p>",
   "creators": [
     {
       "name": "Parrott, Jaxon",
-      "affiliation": "Machine Relations Research"
+      "affiliation": "Machine Relations Research",
+      "orcid": "0009-0004-0039-3913"
     }
   ],
   "keywords": [
@@ -17,8 +18,8 @@
     "mri-dataset-concept"
   ],
   "license": "cc-by-4.0",
-  "version": "mri_score_v2.1+2026-10-27+06e001ee9f9a",
-  "publication_date": "2026-10-27",
+  "version": "mri_score_v2.1+2026-10-29+225b998b091e",
+  "publication_date": "2026-10-29",
   "language": "eng",
   "access_right": "open",
   "notes": "mri-dataset-concept mri_dataset_publication_v1",