{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4074348,0.002084108,0.004682351,0.03286883,0.002197404,0.009357393,0.003553897,0.003203497,0.002824303],"category_scores_gemma":[0.7432731,0.001519062,0.005772945,0.02300398,0.003074035,0.01015496,0.004374498,0.001703135,0.001182854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00420209,"about_ca_system_score_gemma":0.007342173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00284517,"about_ca_topic_score_gemma":0.004366223,"domain_scores_codex":[0.6430652,0.2008088,0.08936387,0.01379172,0.05125481,0.001715649],"domain_scores_gemma":[0.07469798,0.8598937,0.0272918,0.02187768,0.01575266,0.0004861022],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003705562,0.0004891011,0.1690111,0.05888134,0.01086092,0.0005122315,0.009326366,0.004195398,0.005540852,0.01272881,0.0204476,0.7043007],"study_design_scores_gemma":[0.003774055,0.004263554,0.3960902,0.05877684,0.05299255,0.004049831,0.0103031,0.04759128,0.04704439,0.1801236,0.1933193,0.001671284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3059777,0.1183252,0.4799332,0.02162384,0.002536496,0.01310481,0.01468034,0.005096914,0.03872161],"genre_scores_gemma":[0.6137642,0.01225622,0.3511386,0.006151559,0.001173921,0.008191662,0.005185944,0.0004216656,0.001716208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5925652,"threshold_uncertainty_score":0.7307384,"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."}}