{"id":"W1523181615","doi":"10.18438/b89042","title":"Thematic Categorization and Analysis of Peer Reviewed Articles in the LISA Database, 2004-2005","year":2009,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorization; Computer science; Information retrieval; Thematic map; Library science; World Wide Web; Artificial intelligence; Cartography; Geography","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01559718,0.0009064457,0.002752902,0.1877554,0.002568573,0.006839462,0.002382415,0.0008693628,0.009857132],"category_scores_gemma":[0.08219702,0.0005713952,0.001859393,0.2136125,0.001026894,0.004259195,0.003230405,0.0006795345,0.005944881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007872191,"about_ca_system_score_gemma":0.02110684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01658173,"about_ca_topic_score_gemma":0.02602347,"domain_scores_codex":[0.9710579,0.003002815,0.01330378,0.001572461,0.0101752,0.0008878375],"domain_scores_gemma":[0.9099476,0.02044729,0.01940718,0.003277239,0.04488871,0.002031927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001137761,0.0002107105,0.08744995,0.112991,0.0008884561,0.001439405,0.01398326,0.0004735555,0.005070808,0.004987234,0.2450038,0.526364],"study_design_scores_gemma":[0.000134327,0.000166418,0.3001254,0.01892675,0.001219358,0.0008244675,0.01131445,0.000760729,0.003368262,0.0009991406,0.6620006,0.0001600371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1409167,0.1151587,0.006524545,0.007338367,0.001806131,0.006559108,0.6681284,0.001878137,0.05169],"genre_scores_gemma":[0.2170655,0.1244999,0.0569105,0.001558165,0.002331525,0.01499633,0.5541072,0.0008163907,0.02771451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8122445,"threshold_uncertainty_score":0.08248675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980525932325027,"score_gpt":0.2611833541649228,"score_spread":0.2413780948416725,"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."}}