{"id":"W4285817825","doi":"10.1007/978-3-031-10986-7_17","title":"CLINER: Clinical Interrogation Named Entity Recognition","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Interrogation; Dialog box; Computer science; Named-entity recognition; Information extraction; Context (archaeology); Task (project management); Artificial intelligence; Exploit; Natural language processing; Information retrieval; World Wide Web; Computer security","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":[0.00215429,0.001369629,0.0009169831,0.00227021,0.0004333109,0.002509019,0.002001497,0.001361987,0.05821047],"category_scores_gemma":[0.004661836,0.0006907301,0.0006683815,0.001904367,0.0004978205,0.002950692,0.002347539,0.001564547,0.05077496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005212827,"about_ca_system_score_gemma":0.0009021333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001052492,"about_ca_topic_score_gemma":0.001410584,"domain_scores_codex":[0.9987679,0.0002972978,0.0001342562,0.0003210573,0.0004038152,0.00007562568],"domain_scores_gemma":[0.9982584,0.0009229364,0.00008777102,0.0003681306,0.0002578666,0.0001048055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001786719,0.00006321251,0.0006785403,0.0003823926,0.00004549185,0.0003249512,0.0001773902,0.001281675,0.006822379,0.01049666,0.4421847,0.5373641],"study_design_scores_gemma":[0.0001010928,0.0001161164,0.002219264,0.0003015858,0.00008765763,0.002673708,0.0002023238,0.05489657,0.04079043,0.04262368,0.8558535,0.0001339885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006864625,0.003485487,0.6776112,0.002877681,0.001855635,0.0007835507,0.03647576,0.1998991,0.07014699],"genre_scores_gemma":[0.07278485,0.002580227,0.7043473,0.002783758,0.001108426,0.0009188334,0.0934938,0.01849214,0.1034907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05821047,"threshold_uncertainty_score":0.1947334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06366758333984053,"score_gpt":0.3129572673007279,"score_spread":0.2492896839608874,"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."}}