{"id":"W2250670243","doi":"","title":"A Machine Learning Approach for Phenotype Name Recognition","year":2012,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Process (computing); Machine learning; Named-entity recognition; Phenotype; Engineering; Gene; Biology","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":[0.000379823,0.0002050102,0.000189003,0.0001137807,0.0002009473,0.00004905975,0.000302963,0.0003115424,0.00001168781],"category_scores_gemma":[0.0001494747,0.0002116586,0.0001266225,0.0001318512,0.0001106502,0.00003768277,0.0001855398,0.0002824035,0.0000429361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003594728,"about_ca_system_score_gemma":0.00003458438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001636818,"about_ca_topic_score_gemma":0.00002655594,"domain_scores_codex":[0.9987301,0.0001581606,0.0001410138,0.0003659847,0.0001394604,0.0004652989],"domain_scores_gemma":[0.9993271,0.00003321109,0.0001093921,0.0002356271,0.00008793648,0.0002067761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000355034,0.0001959158,0.9678804,0.00007784452,0.0001111109,0.00000468693,0.0001797016,0.00001337328,0.01207174,0.00001547858,0.000005448874,0.01908932],"study_design_scores_gemma":[0.006002543,0.002031137,0.6323843,0.0001480156,0.0004142967,0.000124521,0.001080662,0.00001708124,0.04119888,0.0001422459,0.3145497,0.001906634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732265,0.0008790999,0.02495617,0.00005234456,0.000172509,0.0002090805,0.00004125695,0.00008334343,0.0003797249],"genre_scores_gemma":[0.9924076,0.0001046841,0.002309032,0.0001859906,0.000328663,0.000004637657,0.0005834379,0.00003449779,0.004041492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.335496,"threshold_uncertainty_score":0.8631188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1095111889960496,"score_gpt":0.3143242810529213,"score_spread":0.2048130920568717,"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."}}