{"id":"W2006840849","doi":"10.1108/00242530510629524","title":"Topic familiarity and its effects on term selection and browsing in a thesaurus‐enhanced search environment","year":2005,"lang":"en","type":"article","venue":"Library Review","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Thesaurus; Information retrieval; Selection (genetic algorithm); Computer science; Originality; Term (time); World Wide Web; Search engine; Subject (documents); Natural language processing; Artificial intelligence; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.008629981,0.0002439624,0.0008916546,0.0008718902,0.0004419503,0.001512112,0.0003570818,0.0004529283,0.002203858],"category_scores_gemma":[0.08173464,0.0002937554,0.0008209254,0.00108637,0.0004642155,0.001416286,0.000863382,0.0003305562,0.0002595952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003576642,"about_ca_system_score_gemma":0.0005965707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083554,"about_ca_topic_score_gemma":0.001484148,"domain_scores_codex":[0.990866,0.005943412,0.001126188,0.0005034225,0.001397574,0.0001634533],"domain_scores_gemma":[0.8552538,0.1237344,0.01345368,0.001975679,0.004417575,0.001164932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0265202,0.002721285,0.4518736,0.02098479,0.002593969,0.001536546,0.02900101,0.002633554,0.06525485,0.0009510088,0.001147569,0.3947816],"study_design_scores_gemma":[0.0007105502,0.02010709,0.9429476,0.001199192,0.003050143,0.00235507,0.006939318,0.003083228,0.01410713,0.0006459674,0.004629962,0.0002248028],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918985,0.003657836,0.00217586,0.00007457411,0.00001056823,0.0001885933,0.00005832693,0.0000392139,0.001896481],"genre_scores_gemma":[0.9947537,0.001309278,0.003201029,0.00004739749,0.00001774466,0.0002392611,0.00005958492,0.00001044212,0.0003615682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008629981,"threshold_uncertainty_score":0.04564023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644140188511563,"score_gpt":0.2499994655643328,"score_spread":0.2335580636792172,"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."}}