{"id":"W2405515544","doi":"","title":"Search filter precision can be improved by NOTing out irrelevant content.","year":2011,"lang":"en","type":"article","venue":"PubMed","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"PsycINFO; CINAHL; Computer science; Information retrieval; MEDLINE; Filter (signal processing); Search engine indexing; Computer vision","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":[0.000350952,0.0001268869,0.0001334472,0.00002525926,0.00006089943,0.00001905547,0.0002378802,0.0001927276,0.00002303583],"category_scores_gemma":[0.0003721402,0.00009921999,0.00006867847,0.00003794179,0.0001353944,0.00000199248,0.0001666989,0.0001251485,0.000005457644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001326703,"about_ca_system_score_gemma":0.00002209635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001353337,"about_ca_topic_score_gemma":0.00006319274,"domain_scores_codex":[0.9988382,0.00005557334,0.0001855951,0.0003374209,0.0001315506,0.0004516669],"domain_scores_gemma":[0.9994387,0.00002304086,0.00004970836,0.0002671029,0.00005939107,0.0001620604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001676312,0.0001035049,0.00479073,0.00001656718,0.00007015727,0.000004722631,0.0003808601,7.995317e-8,0.2994679,0.000005005882,0.01666214,0.6783307],"study_design_scores_gemma":[0.001044666,0.0002571924,0.04738085,0.000009795557,0.00002223387,0.000009358999,0.0003860247,0.00004647392,0.8413323,0.00002558774,0.1091515,0.0003340618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933856,0.0007516728,0.001121778,0.0007978422,0.0004618892,0.0004431461,0.00007320723,0.00005052323,0.002914396],"genre_scores_gemma":[0.9928356,0.00005713153,0.001060909,0.000580077,0.0001193343,0.0002391586,0.0000772875,0.00001755038,0.005012906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6779967,"threshold_uncertainty_score":0.4046074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062545983919475,"score_gpt":0.2632409407344072,"score_spread":0.1569863423424597,"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."}}