{"id":"W4376638894","doi":"10.18280/isi.280206","title":"Metadata Analysis to Get Insight into Drug Resistant Ovarian Cancer","year":2023,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Engineering Research Board","keywords":"Metadata; Ovarian cancer; Drug; Cancer; Computer science; World Wide Web; Oncology; Information retrieval; Medicine; Internal medicine; Pharmacology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257695,0.0005569134,0.0005494675,0.01243413,0.0006349234,0.001838106,0.0005997745,0.0006216772,0.002604119],"category_scores_gemma":[0.004595752,0.0001441703,0.0008742528,0.008601861,0.0003421227,0.001853606,0.001109114,0.0004872005,0.001378585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009724791,"about_ca_system_score_gemma":0.00170852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007045948,"about_ca_topic_score_gemma":0.008455892,"domain_scores_codex":[0.9987946,0.000148354,0.0002917114,0.0002806975,0.0003783088,0.0001063292],"domain_scores_gemma":[0.9972326,0.0008871329,0.000571687,0.0004544128,0.0006943279,0.0001598401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001249686,0.0004382296,0.1805656,0.00490942,0.0006171134,0.005116279,0.002371598,0.01010175,0.08519505,0.02783453,0.05741804,0.6241828],"study_design_scores_gemma":[0.0001044799,0.000492722,0.1986924,0.001648476,0.001068976,0.006448927,0.006817468,0.105248,0.06752196,0.06571007,0.5459231,0.0003233181],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2543339,0.01308341,0.2975244,0.004168058,0.0005510955,0.001143322,0.3858863,0.0182147,0.02509482],"genre_scores_gemma":[0.4330189,0.005653995,0.2298664,0.0006125089,0.0002024557,0.0005718016,0.3244179,0.000588436,0.005067548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01243413,"threshold_uncertainty_score":0.01400989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751409881516867,"score_gpt":0.2757245343854793,"score_spread":0.2582104355703106,"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."}}