{"id":"W6944947099","doi":"10.25345/c5t14v09h","title":"MassIVE MSV000093308 - Lambert_SAINT5178_TurboID_Fusion_P134_VS12","year":2023,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"Research Data Management Practices","field":"Computer Science","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","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.001497283,0.0005982587,0.0005747055,0.0009842223,0.0003431977,0.004104808,0.01057017,0.0003547966,0.0008370313],"category_scores_gemma":[0.00196647,0.0005477676,0.0002167423,0.001635035,0.0001470936,0.01068,0.009545382,0.001218825,0.03251423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548437,"about_ca_system_score_gemma":0.0002510452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001146639,"about_ca_topic_score_gemma":0.001153387,"domain_scores_codex":[0.9941949,0.0004719082,0.0006180709,0.001721317,0.001887156,0.001106612],"domain_scores_gemma":[0.9924979,0.001085372,0.0005353831,0.005330884,0.0001659775,0.0003844901],"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":[0.000009776611,0.00009198382,0.000003653141,0.0001512894,0.0001312408,0.001292761,0.00001451568,0.000008573431,0.000003472491,0.005858991,0.9907953,0.001638479],"study_design_scores_gemma":[0.0002599514,0.0001022299,0.0001380372,0.000101563,0.00003899484,0.0000108954,0.00002749988,0.0003101415,0.00001094105,0.0007658108,0.9976385,0.0005954235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002636066,0.0002359237,0.003252351,0.002551927,0.002327917,0.0007194217,0.9868497,0.0005882946,0.00347186],"genre_scores_gemma":[0.000002390605,0.003144796,0.001864756,0.001122804,0.0004882022,0.0001692975,0.9756577,0.00005265118,0.01749741],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0316772,"threshold_uncertainty_score":0.9996974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06610809690814666,"score_gpt":0.3500224130860414,"score_spread":0.2839143161778948,"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."}}