{"id":"W4237049540","doi":"10.1353/ils.2011.0020","title":"Text mining and information retrieval","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Information and Library Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Information retrieval; Computer science; Natural language processing; Data science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002234408,0.0006057234,0.001481988,0.008151324,0.001188591,0.004521198,0.001136969,0.001478234,0.01127414],"category_scores_gemma":[0.008214983,0.0004562402,0.0008990861,0.01059369,0.002026466,0.008021299,0.0009812345,0.001066381,0.006456866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001535063,"about_ca_system_score_gemma":0.001963152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005364001,"about_ca_topic_score_gemma":0.004190096,"domain_scores_codex":[0.997977,0.0005280777,0.000243521,0.0002704544,0.0009018502,0.00007904568],"domain_scores_gemma":[0.9960898,0.002359027,0.0002868359,0.0004267082,0.0007573786,0.00008025114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009665254,0.0001659531,0.001662859,0.001692163,0.0001515731,0.0002625493,0.0003813953,0.003544616,0.003783664,0.183162,0.05234708,0.7527496],"study_design_scores_gemma":[0.00004024264,0.00006217853,0.003077174,0.0004780401,0.0001441494,0.0006181816,0.000549686,0.04181003,0.006460819,0.6434893,0.3032009,0.00006929091],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02420923,0.1732213,0.6772135,0.02885792,0.003709776,0.0005448742,0.004266865,0.004197645,0.08377893],"genre_scores_gemma":[0.3045535,0.09386081,0.4608434,0.004098386,0.00569838,0.0006510572,0.009724637,0.000690289,0.1198796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01127414,"threshold_uncertainty_score":0.03771573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346260354532961,"score_gpt":0.179926310541338,"score_spread":0.1664637069960084,"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."}}