{"id":"W2037374844","doi":"10.3115/1572364.1572383","title":"Identifying interaction sentences from biological literature using automatically extracted patterns","year":2009,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Preprocessor; Sentence; Task (project management); Artificial intelligence; Identification (biology); Natural language processing; Pairwise comparison; Key (lock); Component (thermodynamics); Quality (philosophy); Pattern recognition (psychology)","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":[0.00009080052,0.0001359393,0.0001360747,0.00003562435,0.00006637176,0.00009624996,0.0001531644,0.0002812847,0.00009854441],"category_scores_gemma":[0.0001599957,0.00009508901,0.00008060997,0.00008140429,0.00005284856,0.000006403654,0.00004913642,0.000143779,0.000006806297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009261603,"about_ca_system_score_gemma":0.00001566094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000309346,"about_ca_topic_score_gemma":0.00001077713,"domain_scores_codex":[0.9990914,0.00006540876,0.0002208043,0.0003180886,0.0001064192,0.0001978609],"domain_scores_gemma":[0.9996085,0.00002678738,0.00006891473,0.0001739195,0.00004886227,0.00007303877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005513469,0.000103437,0.008104501,0.000005945996,0.00004275621,0.00002583536,0.0001313858,0.00000441237,0.9251152,0.00003867107,0.0004690161,0.06590375],"study_design_scores_gemma":[0.001823057,0.001978076,0.6346282,0.000897723,0.0001061925,0.0003391668,0.003560782,0.01269129,0.3232889,0.004415251,0.01470353,0.001567795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500309,0.0004851726,0.04850483,0.0003244295,0.0002422089,0.00005353088,0.00001564062,0.00007483261,0.0002684885],"genre_scores_gemma":[0.9793378,0.0001051087,0.01933607,0.0006305577,0.0002787914,0.000001716693,0.0002070757,0.000004783018,0.00009814944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6265237,"threshold_uncertainty_score":0.3877617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05341698554228467,"score_gpt":0.3370121518042756,"score_spread":0.2835951662619909,"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."}}