{"id":"W6945927600","doi":"10.25345/c5tx21","title":"MassIVE MSV000085132 - Low input proteomics for sorted mammary cells","year":2020,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Proteomics; Transcriptome; Cell culture; Cell","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":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0003376592,0.001345581,0.001506884,0.0005364566,0.0002499299,0.0003328258,0.002323955,0.001208136,0.00110339],"category_scores_gemma":[0.0005421546,0.001423414,0.0006398501,0.0007131759,0.0002444781,0.0003092904,0.0009322306,0.001410287,0.03207331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005127862,"about_ca_system_score_gemma":0.0009255769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008256462,"about_ca_topic_score_gemma":0.0003091161,"domain_scores_codex":[0.994754,0.000233481,0.001065066,0.001785168,0.0008585966,0.001303689],"domain_scores_gemma":[0.9953451,0.0003015849,0.00114884,0.002292174,0.0003037695,0.000608582],"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":[0.0004833222,0.0002702302,0.000002607806,0.001455543,0.0005057878,0.0004212565,0.00004708659,0.00002758867,0.004353635,0.00002668732,0.9922294,0.0001768431],"study_design_scores_gemma":[0.001849738,0.0003409419,0.00001256972,0.0003059258,0.0004874563,0.00001397425,0.00002454499,0.0003029788,0.0104086,0.0003709779,0.9843451,0.001537126],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003756855,0.0001607708,0.0004455149,0.0001293772,0.001203016,0.006093152,0.9912415,0.0004910888,0.000198046],"genre_scores_gemma":[0.00001198148,0.00009419507,0.005868461,0.001153108,0.001449156,0.00149572,0.9887419,0.0005411521,0.0006443504],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03096992,"threshold_uncertainty_score":0.9999295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867979536777549,"score_gpt":0.2529290319851559,"score_spread":0.2342492366173804,"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."}}