{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008033797,0.003524253,0.001935934,0.003678865,0.001510405,0.002582126,0.00357077,0.003195759,0.1274334],"category_scores_gemma":[0.004610797,0.0008103955,0.001703433,0.00442619,0.0006277945,0.001260282,0.001974076,0.001849508,0.1539736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001338854,"about_ca_system_score_gemma":0.002605163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02724181,"about_ca_topic_score_gemma":0.0484383,"domain_scores_codex":[0.9990219,0.0001257607,0.00006329593,0.0003670024,0.0002239591,0.0001981257],"domain_scores_gemma":[0.998539,0.0004359951,0.00009635566,0.0003430623,0.0003648086,0.0002208023],"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":[0.0001314774,0.00002414565,0.0006495431,0.0004987692,0.00003723734,0.0000274072,0.00002133691,0.0002507802,0.0004348382,0.0003754245,0.9953453,0.002203739],"study_design_scores_gemma":[0.0006093576,0.0000519276,0.004053011,0.0003173745,0.000101342,0.0001430659,0.0001076668,0.001329305,0.001428436,0.002308224,0.9894999,0.00005034983],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004674843,0.0002489856,0.0001688796,0.0001196648,0.00007337853,0.00001599491,0.9950451,0.001818369,0.002042185],"genre_scores_gemma":[0.0007017302,0.00006889465,0.0004133068,0.00008891575,0.00001043288,0.00004048212,0.9974403,0.0002370356,0.0009988472],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8725666,"threshold_uncertainty_score":0.4263071,"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."}}