{"id":"W6907779917","doi":"10.25345/c5zs2kq86","title":"MassIVE MSV000094847 - CD36 interactome using biotinylation by antibody recognition","year":2024,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Biotinylation; Interactome; Antibody; Transcriptome","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":[0.0004435309,0.0009631793,0.0008100726,0.001374571,0.0001928228,0.0006022977,0.0005755537,0.0008647369,0.008843211],"category_scores_gemma":[0.0003866511,0.001004533,0.0003363413,0.001125806,0.000167685,0.0006683579,0.0003953452,0.001451381,0.1595227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008779391,"about_ca_system_score_gemma":0.0001859669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005219954,"about_ca_topic_score_gemma":0.0003046326,"domain_scores_codex":[0.9957411,0.0003329708,0.0009521401,0.001361292,0.0008285642,0.0007839644],"domain_scores_gemma":[0.9974422,0.0002188922,0.0008507645,0.0009989714,0.000253861,0.0002353203],"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":[0.00008892148,0.0001667358,0.00002400106,0.0004607986,0.0002975872,0.0001958799,0.00005210249,0.000005382497,0.0208824,8.7669e-7,0.9769952,0.0008300734],"study_design_scores_gemma":[0.0004212675,0.0001157938,0.00004262178,0.002072217,0.000893305,0.00006521905,0.00009283461,0.0005119404,0.001963182,0.0002565363,0.9923786,0.001186461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005702171,0.0009769925,0.00002940524,0.00002972169,0.002933975,0.0007957638,0.9887181,0.0004280019,0.0003858884],"genre_scores_gemma":[0.001258402,0.00012706,0.0002481047,0.0001440673,0.001146807,0.00002775938,0.9965305,0.0003573641,0.0001598885],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1506795,"threshold_uncertainty_score":0.9992405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03362488358586098,"score_gpt":0.3398086764686257,"score_spread":0.3061837928827648,"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."}}