{"id":"W2064303623","doi":"10.1016/j.artmed.2004.12.002","title":"Identification of signatures in biomedical spectra using domain knowledge","year":2005,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Artificial intelligence; Classifier (UML); Computer science; Support vector machine; Pattern recognition (psychology); Machine learning; Domain knowledge; Feature vector; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001380854,0.0002262384,0.0006827752,0.001060651,0.00004265222,0.000003919279,0.0001756763,0.0001334323,0.0003772753],"category_scores_gemma":[0.001174217,0.0001778608,0.00006880014,0.001554878,0.0009092203,0.0001082603,0.00003563803,0.0004910857,0.00003266822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002476386,"about_ca_system_score_gemma":0.0001454127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00022337,"about_ca_topic_score_gemma":0.0006878325,"domain_scores_codex":[0.9970349,0.00009539139,0.001534047,0.0003929562,0.000594899,0.0003478074],"domain_scores_gemma":[0.9988424,0.000383839,0.0001914015,0.0002840705,0.0001614071,0.0001368911],"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":[0.0009873923,0.003616592,0.01586279,0.0005991011,0.0001176666,0.0003328671,0.02438647,0.002227885,0.7814179,0.0685778,0.001329654,0.1005439],"study_design_scores_gemma":[0.004208004,0.003791425,0.2609209,0.009916959,0.000495925,0.0004152668,0.03507476,0.2098361,0.2584934,0.2092657,0.0059588,0.001622788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716781,0.0028273,0.01047248,0.01223512,0.000505515,0.0005699575,0.00000623919,0.00004121758,0.001664084],"genre_scores_gemma":[0.9959338,0.0001410185,0.0023164,0.0002049318,0.0013012,0.00001826638,0.0000170082,0.0000214306,0.00004599864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5229245,"threshold_uncertainty_score":0.7252955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.071379303079704,"score_gpt":0.3927546076722171,"score_spread":0.3213753045925132,"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."}}