{"id":"W4393408774","doi":"10.1101/2024.03.27.587041","title":"Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Common Fund; National Institutes of Health; NIH Office of the Director; Canadian Institute for Advanced Research","keywords":"Atlas (anatomy); Computer science; Cartography; Geography; Biology; Anatomy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007478904,0.001935592,0.002024335,0.0090515,0.001529408,0.005439136,0.004462157,0.001942655,0.06811021],"category_scores_gemma":[0.02322092,0.001672441,0.002105581,0.01262748,0.0008264596,0.003780103,0.00605766,0.002959827,0.0495058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001888718,"about_ca_system_score_gemma":0.00555201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01625576,"about_ca_topic_score_gemma":0.01276615,"domain_scores_codex":[0.9970087,0.000966732,0.0003841247,0.0007207949,0.0007214486,0.0001981471],"domain_scores_gemma":[0.9943365,0.001758861,0.0004557679,0.001321423,0.001736404,0.0003909386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002225638,0.00002547739,0.001932876,0.002007943,0.0001948865,0.0002468276,0.0005662779,0.002041954,0.00132047,0.0133274,0.9071559,0.07095746],"study_design_scores_gemma":[0.00008568612,0.0000293292,0.004004297,0.0007055695,0.0001554785,0.0006946758,0.0001881607,0.002229412,0.002198828,0.02028379,0.969308,0.0001168634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.003349328,0.005840695,0.2408779,0.002829432,0.001078362,0.001166177,0.634303,0.07403813,0.03651709],"genre_scores_gemma":[0.02064798,0.005785803,0.3032577,0.001769655,0.0004722389,0.006660888,0.620804,0.02763272,0.01296911],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06811021,"threshold_uncertainty_score":0.2278513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714348019966578,"score_gpt":0.2573146904181123,"score_spread":0.2401712102184465,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}