{"id":"W6958478425","doi":"10.6084/m9.figshare.13265976.v1","title":"Additional file 10 of Development of a 3D functional assay and identification of biomarkers, predictive for response of high-grade serous ovarian cancer (HGSOC) patients to poly-ADP ribose polymerase inhibitors (PARPis): targeted therapy","year":2020,"lang":"en","type":"other","venue":"Figshare","topic":"Alexander von Humboldt Studies","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hôtel-Dieu de Québec","funders":"","keywords":"Serous ovarian cancer; Identification (biology); Ovarian cancer; Polymerase; Serous fluid; Cancer","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":[],"category_scores_codex":[0.00007572935,0.0002579255,0.0005415958,0.0002864068,0.00007184219,0.000002787183,0.0001426206,0.0003392507,0.6839624],"category_scores_gemma":[0.001037592,0.0002495094,0.0000909513,0.0001962862,0.00008871851,0.00003509137,0.000114608,0.00009549932,0.00004110502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005738729,"about_ca_system_score_gemma":0.0004523674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008120436,"about_ca_topic_score_gemma":0.0000523206,"domain_scores_codex":[0.998511,0.0001812293,0.0006373647,0.0003719341,0.0001281709,0.0001703235],"domain_scores_gemma":[0.9975802,0.0007873349,0.001092927,0.0001727368,0.0003356877,0.00003110278],"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":[0.003244376,0.000226166,0.00006123172,0.0002776263,0.001743965,2.199579e-7,0.000580058,4.216099e-7,0.01079363,7.599457e-7,0.9813386,0.001732896],"study_design_scores_gemma":[0.003298848,0.0006301208,0.1048429,0.003041053,0.0000891415,0.000001079073,0.0001608841,0.000001691386,0.1125894,0.000006215844,0.7749633,0.0003753647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000639981,0.00217897,0.0000136445,0.00004266558,0.0001596705,0.000917437,0.9958735,0.00002650863,0.0001476272],"genre_scores_gemma":[0.01930413,0.00001296499,0.0006751108,0.000037617,0.00007682065,0.001949521,0.9615102,0.000113439,0.01632015],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6839213,"threshold_uncertainty_score":0.9999957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298633939786497,"score_gpt":0.2388754437837726,"score_spread":0.2158891043859076,"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."}}