{"id":"W4296167973","doi":"10.20944/preprints202207.0160.v2","title":"NIH SenNet Consortium: Mapping Senescent Cells in the Human Body to Understand Health and Disease","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"Office of Strategic Coordination; NIH Office of the Director; Common Fund; National Institutes of Health","keywords":"Leverage (statistics); Computational biology; Computer science; Data science; Bioinformatics; Medicine; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.006875587,0.001442592,0.0008903832,0.003391021,0.001187004,0.002254,0.001565,0.001053237,0.01213096],"category_scores_gemma":[0.005402394,0.0004078916,0.0009782214,0.003635165,0.0007128959,0.001237186,0.004529367,0.001074443,0.007794268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001599585,"about_ca_system_score_gemma":0.006320902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01893637,"about_ca_topic_score_gemma":0.03298417,"domain_scores_codex":[0.9981857,0.000419461,0.0001073519,0.0003693996,0.0007654638,0.0001526651],"domain_scores_gemma":[0.9959991,0.0006316041,0.0002709374,0.001058063,0.001292179,0.0007481478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009199927,0.00009947767,0.01032751,0.00111321,0.0001990507,0.0002668951,0.0005580485,0.002935192,0.0235475,0.01241464,0.840875,0.1067435],"study_design_scores_gemma":[0.0002543275,0.0001528121,0.02568926,0.0004436435,0.0001820942,0.0004355972,0.0005248716,0.006622147,0.0166165,0.02521422,0.923742,0.0001225747],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02936165,0.006409159,0.1081561,0.01070528,0.002074801,0.000849863,0.7549013,0.03862876,0.04891305],"genre_scores_gemma":[0.04555693,0.003489254,0.1959619,0.001817381,0.0004585891,0.001989759,0.7268858,0.005270257,0.01857025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01893637,"threshold_uncertainty_score":0.04058218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1121692549013726,"score_gpt":0.3354487097888333,"score_spread":0.2232794548874608,"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."}}