{"id":"W7162760387","doi":"10.7939/84285","title":"National Core Library Statistics Program: Statistical Report, 1999: Cultural and Economic Impact of Libraries on Canada","year":2002,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Economic impact analysis; Core (optical fiber); Statistical analysis; Economic statistics; International comparisons; Economic data","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":true,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"other","about_ca_system":false,"about_ca_topic":true,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004481948,0.001210875,0.001459734,0.01443713,0.004661254,0.004228268,0.003746417,0.0009471019,0.009919681],"category_scores_gemma":[0.02468085,0.001212195,0.001486857,0.03706355,0.0008403627,0.001655545,0.00250921,0.002598363,0.002332183],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09095892,"about_ca_system_score_gemma":0.2670258,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9981068,"about_ca_topic_score_gemma":0.9984182,"domain_scores_codex":[0.9918434,0.0004965567,0.0007157238,0.0002638247,0.005580653,0.001099897],"domain_scores_gemma":[0.9456255,0.002647954,0.002245389,0.0008383052,0.04562147,0.003021324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003250496,0.0002195362,0.2196498,0.0008207049,0.0004031951,0.000108698,0.0006135406,0.001758393,0.0001314527,0.002407354,0.740447,0.03311521],"study_design_scores_gemma":[0.000101801,0.00004252879,0.8787792,0.0003296762,0.0002965664,0.00004659933,0.002219958,0.001532863,0.0005710257,0.0003559033,0.1156177,0.0001061107],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03441697,0.001995637,0.0006737482,0.003007879,0.0003892846,0.0006611813,0.9408416,0.00049326,0.01752036],"genre_scores_gemma":[0.1503173,0.009257473,0.007882145,0.001811034,0.000212506,0.001366649,0.7236997,0.0005724064,0.1048808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9957717,"threshold_uncertainty_score":0.6599562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03026040578353959,"score_gpt":0.2624782603647426,"score_spread":0.232217854581203,"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."}}