{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004778994,0.001272418,0.001396632,0.0007577866,0.0001553449,0.0002290073,0.002211072,0.001222654,0.02901762],"category_scores_gemma":[0.0003613668,0.001177511,0.0005290658,0.0005592355,0.0002744455,0.0002437286,0.0008506379,0.00147612,0.4497195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004866757,"about_ca_system_score_gemma":0.000414306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003319879,"about_ca_topic_score_gemma":0.001005654,"domain_scores_codex":[0.9946239,0.0002928425,0.0007967799,0.001598604,0.001364695,0.001323181],"domain_scores_gemma":[0.9943159,0.0002572191,0.0009122204,0.003904375,0.0002236438,0.0003866257],"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":[0.0001018587,0.0001732397,0.00005135847,0.0002743924,0.0003264821,0.0002152943,0.00002349961,0.000009642158,0.0002839746,0.00005946096,0.9983706,0.0001102374],"study_design_scores_gemma":[0.0008834161,0.0001654394,0.0001512938,0.0002297616,0.0004232477,0.00003402209,0.00004747126,0.00001235902,0.000257632,0.00007984837,0.9962378,0.001477672],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009403444,0.0008163379,0.000002258655,0.00005857474,0.002981099,0.001178379,0.9901264,0.0002687915,0.004474121],"genre_scores_gemma":[0.00008276819,0.0001101786,0.000104359,0.0004785261,0.001112484,0.00007355828,0.9913797,0.0003479369,0.006310518],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4207019,"threshold_uncertainty_score":0.9990675,"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."}}