{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008121169,0.0002997309,0.0004279396,0.000602388,0.0003326383,0.0003503222,0.0004409156,0.0003813609,0.0001102364],"category_scores_gemma":[0.0007374994,0.0002879725,0.0002225383,0.002556438,0.000320714,0.0001431859,0.0003835193,0.0001700455,0.00026863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156032,"about_ca_system_score_gemma":0.0002709917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00271165,"about_ca_topic_score_gemma":0.00206096,"domain_scores_codex":[0.9978244,0.0001634696,0.0007584848,0.0003391506,0.0003482219,0.0005663253],"domain_scores_gemma":[0.9985313,0.00005157298,0.00026297,0.0006009546,0.0002894866,0.0002636565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003892771,0.00006862402,0.003145673,0.001094547,0.004103627,0.00002712673,0.0273512,0.003314078,0.008682555,0.00227863,0.08743728,0.8621074],"study_design_scores_gemma":[0.0003975024,0.0001851097,0.01138954,0.0002079109,0.0006251476,0.000005301364,0.002224857,0.002476339,0.006831101,0.0005623746,0.9746026,0.0004922108],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8004201,0.03600928,0.1252615,0.01800914,0.005257729,0.00122193,0.002056903,0.0005379919,0.01122537],"genre_scores_gemma":[0.975764,0.003103528,0.007616081,0.001393116,0.0004416776,0.0001894708,0.002966529,0.00002938975,0.008496189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8871653,"threshold_uncertainty_score":0.9999573,"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."}}