{"id":"W6945104542","doi":"10.25345/c5px7p","title":"MassIVE MSV000085908 - Nabeel-Shah_RebL1_characterization_P108_VS6","year":2020,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","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":[0.0002164288,0.001259288,0.001358707,0.0006116159,0.0002723727,0.0003818737,0.00181047,0.0009386254,0.03485848],"category_scores_gemma":[0.001176514,0.001310576,0.0004452227,0.001211863,0.0002832303,0.0003911946,0.0008146758,0.001487994,0.2551866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003932669,"about_ca_system_score_gemma":0.000464329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006840655,"about_ca_topic_score_gemma":0.0001637326,"domain_scores_codex":[0.9945492,0.0003618961,0.001103527,0.001630623,0.001318101,0.001036676],"domain_scores_gemma":[0.9955921,0.0002412201,0.001214179,0.001928264,0.0002877398,0.0007365352],"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":[0.0001099822,0.0001705899,0.00003514583,0.0003075247,0.0003056615,0.0006416472,0.00007041884,0.00000486914,0.001025535,0.00004706613,0.9971355,0.0001460873],"study_design_scores_gemma":[0.0006132596,0.0001294432,0.0004647125,0.0002602233,0.000340645,0.00003079071,0.00004658092,0.00002548549,0.0002774786,0.00005320769,0.9964113,0.001346897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004701556,0.0001943059,0.00001559909,0.0002298235,0.00156217,0.0009463436,0.9954376,0.0006462373,0.0009209453],"genre_scores_gemma":[0.000128367,0.0001596131,0.0001153272,0.001996363,0.001921964,0.0001776389,0.9940699,0.0004228901,0.001007935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2203282,"threshold_uncertainty_score":0.9989344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02188356342365498,"score_gpt":0.2652500021703609,"score_spread":0.2433664387467059,"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."}}