{"id":"W4225680068","doi":"10.29173/jchla29607","title":"Electronic Resources in Medical Libraries: Issues and Solutions","year":2022,"lang":"fr","type":"article","venue":"Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Library science; Computer science; Data science; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01188212,0.0003079868,0.0006258656,0.0007046682,0.003264149,0.004635027,0.001523063,0.0004440846,0.0007292546],"category_scores_gemma":[0.007467113,0.0003031275,0.0002337348,0.001899115,0.0001134979,0.00362398,0.0004100689,0.003180751,8.865126e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.04471983,"about_ca_system_score_gemma":0.1446013,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3408421,"about_ca_topic_score_gemma":0.6001443,"domain_scores_codex":[0.9876709,0.006112668,0.001555788,0.0002617872,0.002380714,0.00201813],"domain_scores_gemma":[0.9911354,0.003541655,0.003069634,0.000192384,0.000354025,0.001706938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003730488,0.00006684949,0.2688449,0.00003444174,0.0002408229,0.0002825662,0.008961796,0.000741922,5.976353e-7,0.01786388,0.7000234,0.002901581],"study_design_scores_gemma":[0.00081743,0.0001728203,0.09717312,0.0001564511,0.00002686773,0.001969039,0.001241247,0.002558836,0.000006484037,0.02178173,0.8738288,0.0002671521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.104345,0.3808779,0.0002983536,0.5062549,0.004085288,0.0003610798,0.0002021291,0.00003981291,0.003535451],"genre_scores_gemma":[0.8060634,0.08170216,0.002311536,0.07495273,0.003951801,0.00002767587,0.0000134967,0.0001009723,0.03087625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7017184,"threshold_uncertainty_score":0.9999421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006627853870777994,"score_gpt":0.2263383142983783,"score_spread":0.2197104604276003,"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."}}