{"id":"W6945191787","doi":"10.25345/c54j0b209","title":"MassIVE MSV000089219 - Mammary Epithelial Subsets by Cell Cycle","year":2022,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"Subterranean biodiversity and taxonomy","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Cell cycle; Cell; Cell cycle progression; Cell culture; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.0005272152,0.002307358,0.001718155,0.003074089,0.0008761098,0.002089118,0.002666391,0.002011795,0.0540451],"category_scores_gemma":[0.002886422,0.0006810238,0.001630492,0.004776927,0.0003557461,0.0007479011,0.001896127,0.001218293,0.05093888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009216074,"about_ca_system_score_gemma":0.001869643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0300579,"about_ca_topic_score_gemma":0.06012273,"domain_scores_codex":[0.9994181,0.00005294441,0.00004197324,0.0002151851,0.0001283845,0.000143405],"domain_scores_gemma":[0.9990043,0.0002496361,0.000101141,0.0002485721,0.0002176873,0.0001787436],"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":[0.0002345309,0.00003061826,0.003463941,0.0008256476,0.000112952,0.00004647617,0.00004705408,0.0006178329,0.0006758224,0.0004258673,0.9898829,0.003636265],"study_design_scores_gemma":[0.000831084,0.00007037834,0.02578523,0.000505305,0.0002325778,0.0002150734,0.0001847138,0.001747159,0.001769817,0.002465097,0.9661302,0.0000634461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005668381,0.0001148976,0.00005615812,0.00004421331,0.00001916096,0.000006022517,0.9982096,0.0004688192,0.0005142859],"genre_scores_gemma":[0.001233279,0.00006823967,0.0002399797,0.00004648746,0.00000768195,0.00003817763,0.9977415,0.00007307753,0.000551599],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0540451,"threshold_uncertainty_score":0.1807988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661452413762162,"score_gpt":0.1801929382669039,"score_spread":0.1635784141292823,"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."}}