{"id":"W2248009881","doi":"10.1109/smc.2015.332","title":"Flexible Concept Matching for Medical Information Retrieval","year":2015,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Matching (statistics); Flexibility (engineering); Information retrieval; Synonym (taxonomy); Identification (biology); Process (computing); Phrase; Artificial intelligence; Domain (mathematical analysis); Data mining; Natural language processing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003514594,0.00006179671,0.00007580109,0.00001739391,0.00003278739,0.00001867819,0.0001287262,0.0001997623,0.00003774716],"category_scores_gemma":[0.0009048313,0.00004741254,0.00003725363,0.00003923105,0.00008069126,0.00000421808,0.00006205628,0.00004926024,0.00001970246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000711292,"about_ca_system_score_gemma":0.0001505178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001183335,"about_ca_topic_score_gemma":0.000003316222,"domain_scores_codex":[0.9994122,0.00001485156,0.0001504618,0.00009233504,0.0001859833,0.0001441926],"domain_scores_gemma":[0.9996074,0.00001922664,0.00003299732,0.0001083392,0.00008171633,0.00015033],"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":[0.0009540545,0.00009648914,0.0004528388,0.00006113434,0.0001106814,0.00000282255,0.001209408,0.00005750394,0.01041215,0.006901351,0.6705489,0.3091926],"study_design_scores_gemma":[0.001551757,0.0005413111,0.00008932266,0.0000104146,0.00000675235,0.00001985703,0.0009638916,0.0003384051,0.06644616,0.001388397,0.9284878,0.0001559666],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1505028,0.0005882721,0.8281529,0.003795872,0.0008588939,0.0002688987,0.00002661257,0.0001445844,0.01566118],"genre_scores_gemma":[0.9683892,0.00002736959,0.02382824,0.003760862,0.0005842504,0.00001599466,0.000444029,0.00001107228,0.002939038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8178864,"threshold_uncertainty_score":0.1933427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578924653828432,"score_gpt":0.3116063538038077,"score_spread":0.2858171072655235,"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."}}