{"id":"W2501995790","doi":"10.1080/01462679.2016.1208132","title":"Using WorldShare Collection Evaluation to Analyze Physical Science and Engineering Monograph Holdings by Discipline","year":2016,"lang":"en","type":"article","venue":"Collection Management","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Collection development; Staffing; Purchasing; Subject (documents); Library science; Academic library; Data collection; Computer science; Multidisciplinary approach; Engineering management; Business; Data science; Operations research; Management; Sociology; Marketing; Engineering; Social science; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000534908,0.0001587178,0.0001236179,0.001215035,0.0006813438,0.0007117742,0.0003272741,0.00002267769,0.00001633085],"category_scores_gemma":[0.00008693289,0.0001288246,0.00003543701,0.007583146,0.00004910865,0.002244523,0.0004886147,0.00003988284,0.000009436024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503653,"about_ca_system_score_gemma":0.00008776442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006981092,"about_ca_topic_score_gemma":0.000002876962,"domain_scores_codex":[0.998069,0.000024724,0.0001873512,0.0005805588,0.0008139715,0.0003243877],"domain_scores_gemma":[0.9992867,0.00004153728,0.000068042,0.0002251693,0.0002057263,0.0001728948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003524662,0.0009525245,0.01459566,0.0002304102,0.0005671756,0.00001760808,0.006083784,0.01188141,0.1640038,0.04867282,0.2469038,0.5057386],"study_design_scores_gemma":[0.002193362,0.0003147676,0.04593026,0.0002203558,0.00006879714,0.00001344903,0.0001703457,0.8789232,0.02109394,0.002777566,0.04729318,0.00100072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5748242,0.00007169273,0.4153515,0.001753195,0.0005703322,0.001185473,0.000003039772,0.0003787026,0.005861823],"genre_scores_gemma":[0.9842085,0.00002090182,0.01053846,0.0001110605,0.00005172059,0.0001397106,0.000001358986,0.00001206439,0.004916193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8670418,"threshold_uncertainty_score":0.6863655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191410945143352,"score_gpt":0.2618652064740287,"score_spread":0.2399510970225952,"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."}}