Datasets:

yhsure
/

BEND2

{
  "schemaVersion": "1.0.0",
  "id": "draft-table-1",
  "title": "BEND 2.0: Benchmarking the Capabilities Needed to Design DNA",
  "shortName": "BEND 2.0",
  "expandedName": "Benchmark for DNA language models",
  "version": "manuscript-1abee7c6",
  "status": "manuscript-snapshot-with-audited-additions",
  "description": "Eight reported endpoints across five capability categories. Read the raw endpoints alongside a transparent, exploratory reference index.",
  "source": {
    "label": "Table 1 snapshot, 22 October 2026, with audited additions",
    "commit": "1abee7c638616c578b2fea497e63b59e68752c01",
    "workingTreeClean": true,
    "tablePath": "tables/bend2-reference-main.tex",
    "tableSha256": "75febce7f238262b435731ba8258f7f647111a6edf83f430f2099ada3eb5c471",
    "recordsSha256": "332d38d328592db946c336c0260d62c4aa02c9be005636e29bea5054fa743c28",
    "snapshotPath": "provenance/table-1.tex",
    "extractedOn": "2026-10-22",
    "precision": "Manuscript display precision; precise source estimates and available intervals are retained for archived and newly evaluated entries.",
    "verification": "Archived Table 1 and source CSV are preserved. Shared checkpoint priors and newly evaluated entries have explicit source mappings in data/evaluation-updates.json; missing-result explanations were checked against evaluation code and archives.",
    "paperUrl": "https://arxiv.org/abs/2611.03806",
    "paperUrlStatus": "Available on arXiv.",
    "codeUrl": "https://github.com/MachineLearningLifeScience/BEND2",
    "codeUrlStatus": "Public benchmark code and evaluation instructions.",
    "researchRepository": "https://github.com/yhsure/genomic-bending"
  },
  "categories": [
    {
      "id": "priors",
      "label": "Sequence priors",
      "color": "#9C7524",
      "description": "Reconstruct a hidden nucleotide from its sequence context."
    },
    {
      "id": "prediction",
      "label": "Functional prediction",
      "color": "#2463A6",
      "description": "Predict the experimental effect of a DNA change."
    },
    {
      "id": "context",
      "label": "Biological context",
      "color": "#19836E",
      "description": "Distinguish effects across cell types and expression profiles across tissues."
    },
    {
      "id": "interactions",
      "label": "Genetic interactions",
      "color": "#7956A5",
      "description": "Predict how a mutation\u2019s effect changes with its genetic background."
    },
    {
      "id": "design",
      "label": "Genomic design",
      "color": "#BE4D35",
      "description": "Choose measured edits or sequences that satisfy a requested response."
    }
  ],
  "tasks": [
    {
      "id": "prior",
      "label": "Prior loss",
      "shortLabel": "Prior loss",
      "categoryId": "priors",
      "description": "Predict the hidden central base of 512 human reference-genome windows. Lower loss means better calibrated conditional probabilities.",
      "metric": "Hidden-base conditional log loss",
      "unit": "bits/base",
      "direction": "lower",
      "precision": 3,
      "datasetId": "grch38",
      "cohort": "512 windows of 1,200 bp from separate 1-Mb genomic blocks; central base hidden.",
      "figure": "Figure 1A",
      "baseline": {
        "value": 1.974,
        "label": "Base composition",
        "kind": "empirical",
        "rationale": "The reported base-composition loss is 1.974 bits/base. This is an empirical baseline on the frozen panel, not the 2-bit uniform prior."
      },
      "cautions": [
        "Interface-sensitive: NTv3 uses 12% additional masking and averages the individual losses of two queries; GENERATORv2 uses temperature-scaled probabilities.",
        "Only rows with a comparable nucleotide-probability interface report this task. Missing prior scores are omitted from the combined score, never imputed."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": null
    },
    {
      "id": "opensplice",
      "label": "Splicing effects",
      "shortLabel": "Splicing",
      "categoryId": "prediction",
      "description": "Predict mutation-induced changes in alternative splicing; correlate within each held-out gene, then average genes equally.",
      "metric": "Macro-gene Spearman correlation",
      "unit": "\u03c1",
      "direction": "higher",
      "precision": 3,
      "datasetId": "opensplice",
      "cohort": "590,104 interventions in 466 genes; five folds hold out complete genes for fitted readouts.",
      "figure": "Figure 2A",
      "baseline": {
        "value": 0,
        "label": "Uninformative rank ordering",
        "kind": "theoretical-null",
        "rationale": "Zero is the expected correlation for an independent random ordering. A constant predictor has undefined Spearman correlation, so zero is a normalization anchor rather than a measured constant baseline."
      },
      "cautions": [
        "Native splice heads retain historical supervision; fitted readouts are trained on the benchmark\u2019s source folds.",
        "OpenSplice is the full gene-held-out endpoint, not a cross-assay transfer score."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 0.809,
        "label": "Experimental reference",
        "caveat": "WT PSI is shared across repeat-specific effects. This is a conditional experimental repeat reference, not a fully independent effect-replicate estimate or ceiling."
      }
    },
    {
      "id": "context",
      "label": "Cell contrast",
      "shortLabel": "Cell contrast",
      "categoryId": "context",
      "description": "Predict the HepG2-minus-K562 difference in a mutation\u2019s reporter effect, then correlate within regulatory elements and average elements equally.",
      "metric": "Macro-element Spearman correlation",
      "unit": "\u03c1",
      "direction": "higher",
      "precision": 3,
      "datasetId": "siraj",
      "cohort": "170,292 substitutions across 128 elements; complete elements and related allelic backgrounds remain together during fitting and evaluation.",
      "figure": "Figure 3B",
      "baseline": {
        "value": 0,
        "label": "Uninformative rank ordering",
        "kind": "theoretical-null",
        "rationale": "Zero is the expected correlation for an independent random ordering; it is not an evaluated constant predictor."
      },
      "cautions": [
        "The experimental reference has physical counts for 127 of 128 elements and uses repeat subsets; model values use the pooled posterior effects."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 0.594,
        "label": "Experimental reference",
        "caveat": "Disjoint RNA/DNA subsets 1\u20132 and 3\u20135; fixed cohort; trimmed count reconstruction. Published model endpoints use pooled posterior effects; reference is not a ceiling. 127/128 loci; one lacks physical counts. Direct-m0 sensitivity: 0.559."
      }
    },
    {
      "id": "tissue_identification",
      "label": "Tissue identification",
      "shortLabel": "Tissue ID",
      "categoryId": "context",
      "description": "Match a predicted expression profile across held-out genes to the correct measured tissue among 51 candidates.",
      "metric": "Top-1 tissue identification accuracy",
      "unit": "%",
      "direction": "higher",
      "precision": 1,
      "datasetId": "gtex",
      "cohort": "5,782 held-out genes and 51 represented tissues; 12,146 training and 1,350 validation genes.",
      "figure": "Appendix tissue figure B",
      "baseline": {
        "value": 1.9607843137254901,
        "label": "Uniform choice among 51 tissues",
        "kind": "exact-expectation",
        "rationale": "The exact chance anchor is 100/51 = 1.960784\u2026%, displayed as 2.0%. Tissue classes receive equal weight."
      },
      "cautions": [
        "This holds out genes within represented tissues; it does not test transfer to unseen tissues.",
        "The 99.0% experimental reference averages both donor-half directions; no one-direction interval is transferred to it."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 99.0,
        "label": "Experimental reference",
        "caveat": "Same 5782 genes and 51 tissues; mean of both directions under the saved donor partition. Reproducibility reference, not a ceiling. Previous one-direction confidence interval not transferred to reciprocal metric."
      }
    },
    {
      "id": "background",
      "label": "Background interaction",
      "shortLabel": "Interaction",
      "categoryId": "interactions",
      "description": "Predict how the same mutation\u2019s effect differs across two parental genetic backgrounds, isolating background dependence.",
      "metric": "Macro-element Spearman correlation",
      "unit": "\u03c1",
      "direction": "higher",
      "precision": 3,
      "datasetId": "siraj",
      "cohort": "68,026 paired mutations in 114 elements; paired backgrounds remain together.",
      "figure": "Figure 4A",
      "baseline": {
        "value": 0,
        "label": "Uninformative rank ordering",
        "kind": "theoretical-null",
        "rationale": "Zero is the expected correlation for an independent random ordering; it is not an evaluated constant predictor."
      },
      "cautions": [
        "The current experimental reference is HepG2\u2013K562 transfer (0.421), not the superseded replicate-subset background value.",
        "Transfer across cells is an experimental comparison, not a noise ceiling."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 0.421,
        "label": "Experimental reference",
        "caveat": "Measured interactions in HepG2 versus K562; within-element Spearman averaged over 114 elements and 68,026 paired mutations. Same reference as Figure 4A; 95% element-bootstrap interval."
      }
    },
    {
      "id": "repair_percentile",
      "label": "Single-edit rank",
      "shortLabel": "Edit rank",
      "categoryId": "design",
      "description": "Choose an alternative single-base edit that matches the effect of a reference edit. Score the first nomination by its measured residual\u2019s rank among eligible candidates.",
      "metric": "Mean measured residual midrank percentile",
      "unit": "percentile",
      "direction": "lower",
      "precision": 1,
      "datasetId": "siraj",
      "cohort": "331 response targets in 110 elements; 578\u2013597 eligible substitutions per element; direct reversion is excluded.",
      "figure": "Figure 5A",
      "baseline": {
        "value": 50,
        "label": "Uniform candidate selection",
        "kind": "exact-expectation",
        "rationale": "Uniform selection has mean measured midrank percentile 50. Lower is better. This is a measured residual rank, not a success percentage."
      },
      "cautions": [
        "All candidate measurements are withheld until evaluation; predictions rank proximity to the model\u2019s own predicted reference-edit effect."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 11.8,
        "label": "Experimental reference",
        "caveat": "Disjoint RNA/DNA subsets 1\u20132 and 3\u20135; fixed cohort; trimmed count reconstruction. Published model endpoints use pooled posterior effects; reference is not a ceiling. All 331 requests/110 loci; reciprocal selection; entire candidate sets retained."
      }
    },
    {
      "id": "cell_selective",
      "label": "Cell selectivity",
      "shortLabel": "Cell selectivity",
      "categoryId": "design",
      "description": "Select one substitution or no edit to increase activity in the target cell while penalizing the absolute effect in the other cell: U = target \u2212 |other|.",
      "metric": "Mean cell-selective utility",
      "unit": "log\u2082 utility",
      "direction": "higher",
      "precision": 3,
      "datasetId": "siraj",
      "cohort": "167,958 substitutions, 280 parental backgrounds and 127 loci; both cells serve as targets; loci receive equal weight.",
      "figure": "Figure 5C",
      "baseline": {
        "value": 0,
        "label": "No edit",
        "kind": "defined-control",
        "rationale": "The protocol defines U(no edit)=0 exactly. The primary penalty is \u03bb=1. Negative measured utility can occur despite predicted abstention because predictions can be wrong."
      },
      "cautions": [
        "Native outputs use held-locus, nonnegative per-cell calibration slopes; they are not zero-shot in this task.",
        "Model utilities use all five RNA repeats; experimental selection uses reciprocal disjoint repeat subsets.",
        "Original confirmatory tests concern within-model controls; a sorted point estimate is not a significance claim."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 0.962,
        "label": "Experimental reference",
        "caveat": "No-edit utility is zero. Reciprocal held-out RNA subsets; model entries use pooled RNA. Not an identical measurement aggregation or a ceiling."
      }
    },
    {
      "id": "atlas_profile",
      "label": "Multi-cell gain",
      "shortLabel": "Multi-cell gain",
      "categoryId": "design",
      "description": "Select ten enhancers for each multi-cell ON/OFF program. Utility rewards the weakest requested activity and penalizes the strongest unwanted activity; report gain over intent-matched random selection.",
      "metric": "Mean utility gain over intent-matched random selection",
      "unit": "log\u2082 gain",
      "direction": "higher",
      "precision": 3,
      "datasetId": "atlas",
      "cohort": "2,411 measured 145-nt candidates, 16 overlapping programs and 12 cell types; select 10 per request. Fit on 5,246 separate source constructs after excluding candidate families.",
      "figure": "Figure 6A",
      "baseline": {
        "value": 0,
        "label": "Intent-matched random selection",
        "kind": "defined-control",
        "rationale": "The reported endpoint subtracts the exact expected utility of uniform selection in each program\u2019s author-intended target cohort. Therefore random gain is exactly zero; absolute utility is a different quantity."
      },
      "cautions": [
        "The native rows and Enformer fit linear heads to native functional outputs on source constructs.",
        "The two RNA-repeat markers are descriptive repeat measurements, not confidence intervals or independent program replications.",
        "Positive gain does not imply positive absolute utility; AlphaGenome fitted has 0.833 gain but \u22120.096 absolute utility."
      ],
      "sourceIds": [
        "table-1",
        "table-1-records",
        "methods"
      ],
      "reference": {
        "value": 1.602,
        "label": "Experimental reference",
        "caveat": "Same16programs,10nominations,reciprocalRNArepeats; gain over intent-matched random."
      }
    }
  ],
  "models": [
    {
      "id": "markov0",
      "label": "Base composition",
      "shortLabel": "Base composition",
      "family": "Composition",
      "kind": "baseline",
      "interface": "sequence",
      "parentModelId": null,
      "parameterLabel": null,
      "description": "Empirical base-composition predictor; no assay labels.",
      "sortOrder": 0
    },
    {
      "id": "evo2_20b",
      "label": "Evo2 20B",
      "shortLabel": "Evo2 20B",
      "family": "Evo2",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "20B",
      "description": "Causal genomic language model; frozen representation readouts on functional tasks.",
      "sortOrder": 1
    },
    {
      "id": "ceno_1b_1m",
      "label": "CENO 1B",
      "shortLabel": "CENO 1B",
      "family": "CENO",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "1B",
      "description": "Genomic sequence model; frozen 1,024-dimensional mean embeddings for fitted tasks.",
      "sortOrder": 2
    },
    {
      "id": "generator_v2_3b",
      "label": "GENERATORv2",
      "shortLabel": "GENERATORv2",
      "family": "GENERATORv2",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "3B",
      "description": "Eukaryote-base causal model with sixmer tokens and nucleotide-factorized predictions.",
      "sortOrder": 3
    },
    {
      "id": "dnabert2",
      "label": "DNABERT-2",
      "shortLabel": "DNABERT-2",
      "family": "DNABERT",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "117M",
      "description": "Masked genomic language model with BPE tokens and frozen representation readouts.",
      "sortOrder": 4
    },
    {
      "id": "ntv3_100m_pre",
      "label": "NTv3 100M pre",
      "shortLabel": "NTv3 100M pre",
      "family": "NTv3",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "100M",
      "description": "Sequence-pretrained NTv3 checkpoint; frozen representation readouts.",
      "sortOrder": 5
    },
    {
      "id": "ntv3_100m_post",
      "label": "NTv3 100M post",
      "shortLabel": "NTv3 100M post",
      "family": "NTv3",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "100M",
      "description": "Functionally post-trained NTv3 checkpoint; frozen hidden representation readouts.",
      "sortOrder": 6
    },
    {
      "id": "ntv3_100m_native",
      "label": "NTv3 100M post \u00b7 native",
      "shortLabel": "NTv3 100M native",
      "family": "NTv3",
      "kind": "foundation",
      "interface": "native",
      "parentModelId": "ntv3_100m_post",
      "parameterLabel": "100M",
      "description": "Same post-trained checkpoint using its native functional outputs; multi-cell still fits a linear head to those outputs.",
      "sortOrder": 7
    },
    {
      "id": "ntv3_650m_pre",
      "label": "NTv3 650M pre",
      "shortLabel": "NTv3 650M pre",
      "family": "NTv3",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "650M",
      "description": "Sequence-pretrained NTv3 checkpoint; frozen representation readouts.",
      "sortOrder": 8
    },
    {
      "id": "ntv3_650m_post",
      "label": "NTv3 650M post",
      "shortLabel": "NTv3 650M post",
      "family": "NTv3",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": "650M",
      "description": "Functionally post-trained NTv3 checkpoint; frozen hidden representation readouts. Fixed 100-point anchor for the exploratory index.",
      "sortOrder": 9
    },
    {
      "id": "ntv3_650m_native",
      "label": "NTv3 650M post \u00b7 native",
      "shortLabel": "NTv3 650M native",
      "family": "NTv3",
      "kind": "foundation",
      "interface": "native",
      "parentModelId": "ntv3_650m_post",
      "parameterLabel": "650M",
      "description": "Same post-trained checkpoint using its native functional outputs; multi-cell still fits a linear head to those outputs.",
      "sortOrder": 10
    },
    {
      "id": "alphagenome_fitted",
      "label": "AlphaGenome",
      "shortLabel": "AlphaGenome",
      "family": "AlphaGenome",
      "kind": "foundation",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": null,
      "description": "Functionally supervised sequence model using fitted linear heads on frozen hidden representations.",
      "sortOrder": 11
    },
    {
      "id": "alphagenome",
      "label": "AlphaGenome \u00b7 native",
      "shortLabel": "AlphaGenome native",
      "family": "AlphaGenome",
      "kind": "foundation",
      "interface": "native",
      "parentModelId": "alphagenome_fitted",
      "parameterLabel": null,
      "description": "Native functional outputs; cell-selective slopes and multi-cell native-output readout are fitted on source data.",
      "sortOrder": 12
    },
    {
      "id": "enformer",
      "label": "Enformer",
      "shortLabel": "Enformer",
      "family": "Enformer",
      "kind": "foundation",
      "interface": "native",
      "parentModelId": null,
      "parameterLabel": null,
      "description": "Native sequence-to-function outputs; multi-cell uses a fitted linear head on native functional features.",
      "sortOrder": 13
    },
    {
      "id": "kmer_3_6",
      "label": "k-mer ridge",
      "shortLabel": "k-mer ridge",
      "family": "k-mer",
      "kind": "baseline",
      "interface": "fitted",
      "parentModelId": null,
      "parameterLabel": null,
      "description": "Ridge regression on 3\u20136-mer features; multi-cell uses strand-invariant 1\u20136-mer frequencies.",
      "sortOrder": 14
    },
    {
      "id": "onehot_resnet",
      "label": "Task-trained ResNet",
      "shortLabel": "ResNet",
      "family": "ResNet",
      "kind": "baseline",
      "interface": "task-trained",
      "parentModelId": null,
      "parameterLabel": null,
      "description": "Supervised one-hot network trained on the task source data; multi-cell uses a three-seed ensemble.",
      "sortOrder": 15
    },
    {
      "id": "spliceai",
      "label": "SpliceAI",
      "shortLabel": "SpliceAI",
      "family": "SpliceAI",
      "kind": "specialist",
      "interface": "native",
      "parentModelId": null,
      "parameterLabel": null,
      "description": "Specialist splice predictor, evaluated using its published minigene score; historical supervision differs from fitted benchmark readouts.",
      "sortOrder": 16
    }
  ],
  "measurements": [
    {
      "modelId": "markov0",
      "taskId": "prior",
      "value": 1.974,
      "displayValue": "1.974",
      "status": "evaluated",
      "interface": "native; 1 queries; other-position mask rate 0.0",
      "sourceValue": 1.9744053548886615,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/prior",
      "notes": [
        "Preserved current manuscript's selected interface; upstream source and interval definitions in source_record"
      ],
      "uncertainty": {
        "level": 0.95,
        "low": 1.9530576068780299,
        "high": 1.9967583599809648,
        "type": "reported pointwise bootstrap interval",
        "samplingUnit": "genomic block",
        "sourceId": "table-1-records",
        "note": "Preserved from the matched source-table record; not an interval for the derived reference index and not evidence for a between-model rank."
      }
    },
    {
      "modelId": "markov0",
      "taskId": "opensplice",
      "value": null,
      "displayValue": "\u2014",
      "status": "unsupported",
      "interface": null,
      "sourceValue": null,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/opensplice",
      "notes": [
        "No matching full OpenSplice gene-held-out result available; transfer scores not substituted."
      ],
      "uncertainty": null,
      "manuscriptStatus": "unsupported",
      "missingReason": "Base composition is a sequence-prior baseline; it does not predict functional outcomes.",
      "extensionId": "coverage-20261027"
    },
    {
      "modelId": "markov0",
      "taskId": "context",
      "value": null,
      "displayValue": "\u2014",
      "status": "unsupported",
      "interface": null,
      "sourceValue": null,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/context",
      "notes": [
        "Preserved current manuscript's selected interface; upstream source and interval definitions in source_record"
      ],
      "uncertainty": null,
      "manuscriptStatus": "unevaluated",
      "missingReason": "Base composition is a sequence-prior baseline; it does not predict functional outcomes.",
      "extensionId": "coverage-20261027"
    },
    {
      "modelId": "markov0",
      "taskId": "tissue_identification",
      "value": null,
      "displayValue": "\u2014",
      "status": "unsupported",
      "interface": null,
      "sourceValue": null,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/tissue_identification",
      "notes": [],
      "uncertainty": null,
      "manuscriptStatus": "unevaluated",
      "missingReason": "Base composition is a sequence-prior baseline; it does not predict functional outcomes.",
      "extensionId": "coverage-20261027"
    },
    {
      "modelId": "markov0",
      "taskId": "background",
      "value": null,
      "displayValue": "\u2014",
      "status": "unsupported",
      "interface": null,
      "sourceValue": null,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/background",
      "notes": [
        "Preserved current manuscript's selected interface; upstream source and interval definitions in source_record"
      ],
      "uncertainty": null,
      "manuscriptStatus": "unevaluated",
      "missingReason": "Base composition is a sequence-prior baseline; it does not predict functional outcomes.",
      "extensionId": "coverage-20261027"
    },
    {
      "modelId": "markov0",
      "taskId": "repair_percentile",
      "value": null,
      "displayValue": "\u2014",
      "status": "unsupported",
      "interface": null,
      "sourceValue": null,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/repair_percentile",
      "notes": [],
      "uncertainty": null,
      "manuscriptStatus": "unevaluated",
      "missingReason": "Base composition is a sequence-prior baseline; it does not predict functional outcomes.",
      "extensionId": "coverage-20261027"
    },
    {
      "modelId": "markov0",
      "taskId": "cell_selective",
      "value": null,
      "displayValue": "\u2014",
      "status": "unsupported",
      "interface": null,
      "sourceValue": null,
      "sourceId": "table-1",
      "sourceRecordId": "markov0/cell_selective",
      "notes": [],
      "uncertainty": null,
      "manuscriptStatus": "unevaluated",
      "missingReason": "Base composition is a sequence-prior baseline; it does not predict functional outcomes.",
      "extensionId": "coverage-20261027"
    },
    {
      "modelId": "markov0",
      "taskId": "atlas_profile",
      "value": null,
      "displayValue": "\u2014",
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        "Preserved current manuscript's selected interface; upstream source and interval definitions in source_record"
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        "level": 0.95,
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        "Retrospective model-roster extension of frozen task; source-only fitting. Pointwise intervals; no expanded confirmatory family. Mean of two RNA-repeat gains over the same 16 requests; repeats are descriptive, not independent uncertainty estimates."
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        "590104 interventions;466 genes; five-fold complete-gene readouts or separately identified native output, as Figure2A."
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        "331 response targets in 110 elements; measured residual midrank scaled to [0,100], lower better; first nomination minimizes model-space distance to its predicted direct-swap effect; no direct reversion"
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        "Retrospective model-roster extension of frozen task; source-only fitting. Pointwise intervals; no expanded confirmatory family. Mean of two RNA-repeat gains over the same 16 requests; repeats are descriptive, not independent uncertainty estimates.",
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      "id": "evo2-tissue-identification-methods",
      "title": "Evo2 extraction and independent evaluation checks",
      "snapshotPath": "provenance/evo2-tissue-20261102/README.md",
      "sha256": "7adc9a70eda713700bd91e5eaa23cc54cedb46ad6e5ffd5cb665a93e36b162cc"
    },
    {
      "id": "evo2-tissue-identification-files",
      "title": "Evo2 evaluation artifact checksums",
      "snapshotPath": "provenance/evo2-tissue-20261102/SHA256SUMS.json",
      "sha256": "a43ae2b3e1594858e8bd37f78165790cf5b63344394c6780f4faf9bc06180051"
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  "scoring": {
    "id": "reference-index-v2",
    "label": "Reference index",
    "status": "exploratory-not-a-paper-metric",
    "referenceModelId": "ntv3_650m_post",
    "defaultTaskIds": [
      "prior",
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    "fullTaskIds": [
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    "defaultCategoryIds": [
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      "interactions",
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    "normalization": "100 * (value - baseline) / (referenceValue - baseline)",
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    "aggregation": "Compute the mean of available normalized task scores within each category, then average available categories equally.",
    "missingPolicy": "Missing tasks are omitted, never zero. Categories with no available tasks are omitted. Task and category coverage are retained in score tooltips and CSV exports. No available results means no score.",
    "clamp": false,
    "weighting": "Equal available categories; equal available endpoints within each category.",
    "uncertainty": null,
    "cautions": [
      "This index is a site exploration aid; it is not a published manuscript endpoint, biological percentage or statistical significance measure.",
      "Scores may be below 0 or above 100. The fixed model reference is not an upper bound.",
      "The 8-endpoint index includes sequence priors as one of five equally weighted available categories. Prior loss is interface-sensitive; rows without nucleotide probabilities receive no imputed prior score.",
      "Category weighting is explicit but does not make overlapping assays statistically independent.",
      "Experimental references are context, not normalization anchors or ceilings.",
      "Ranking uses canonical Table 1 precision; ties at that precision are retained."
    ]
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      "label": "Response matching by testing budget",
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      "note": "Separate success-rate endpoint underlying the response-matching budget figure; a subset of the current model roster plus uniform selection. Each model\u2019s companion residual-rank endpoint matches the current Table 1 precise value. It does not replace the Table 1 residual-rank endpoint.",
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  "limitations": [
    "This site compares the evaluated model\u2013interface pipelines; input context, historical supervision and readout training differ.",
    "Results use held-out measurements from assayed libraries; no new prospective wet-lab experiments were conducted for the site.",
    "Source confidence intervals describe individual task estimates. No confidence interval or significance claim is attached to an aggregate index.",
    "Native and fitted outputs are distinct rows; native multi-cell entries still require fitted readouts."
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  "evaluationUpdateId": "coverage-20261027"
}