{"id":"W6907754789","doi":"10.25345/c5gp7d","title":"MassIVE MSV000083414 - Meant_RSK_BioID_2019","year":2019,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Identification (biology); Process (computing); Work (physics); Set (abstract data type)","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":[0.001036115,0.003563643,0.002322588,0.004421469,0.001586204,0.003634771,0.003819332,0.003435703,0.1857723],"category_scores_gemma":[0.007325937,0.001116185,0.002091374,0.005429803,0.0006505924,0.001451792,0.002606603,0.001993746,0.2009875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187372,"about_ca_system_score_gemma":0.003844012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02755484,"about_ca_topic_score_gemma":0.04711664,"domain_scores_codex":[0.9988508,0.0001470021,0.00009731261,0.0004035448,0.0002598189,0.0002415418],"domain_scores_gemma":[0.9976332,0.0007911617,0.0001731126,0.0005505779,0.0005006413,0.0003511538],"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":[0.00008979143,0.00001099777,0.0003950098,0.0004540101,0.0000314203,0.00001711203,0.00001417679,0.0001618041,0.0001550561,0.0003306619,0.9971697,0.001170347],"study_design_scores_gemma":[0.0006094099,0.00003884342,0.002665972,0.0003822015,0.00010192,0.00009284795,0.00006878725,0.0007215083,0.000862458,0.002917622,0.9914798,0.00005863173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001250148,0.0001333654,0.00008073905,0.00009916375,0.00006463849,0.00001098341,0.9972131,0.0009901143,0.001283054],"genre_scores_gemma":[0.0004716278,0.00008509386,0.0003423115,0.0001447498,0.00001706478,0.00005545444,0.9974759,0.0002552556,0.001152498],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1857723,"threshold_uncertainty_score":0.6214702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02303255520394727,"score_gpt":0.2757252874365162,"score_spread":0.2526927322325689,"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."}}