{"id":"W6887675513","doi":"10.17605/osf.io/4hwa2","title":"Subset of CARL Interlibrary Loan Statistics in CSV format","year":2018,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interlibrary loan; Data collection; Loan; Statistical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001011477,0.0001992068,0.0002786411,0.0003337453,0.00008792568,0.0002829452,0.001791739,0.00009989219,0.04778292],"category_scores_gemma":[0.0004794189,0.0002073263,0.00007325054,0.0006762152,0.0001819132,0.002119216,0.002271531,0.0001867971,0.1374625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007815442,"about_ca_system_score_gemma":0.0002318021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006640718,"about_ca_topic_score_gemma":0.00006002807,"domain_scores_codex":[0.9975296,0.0001825252,0.0006059918,0.0009551743,0.0003587794,0.0003678783],"domain_scores_gemma":[0.9975394,0.0002868721,0.000207608,0.001708762,0.0001031873,0.0001541763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003161491,0.0005827171,0.4422175,0.0002325696,0.0001446248,0.00007476899,0.0186219,0.0001745087,0.001136052,0.06314735,0.2583855,0.2149663],"study_design_scores_gemma":[0.002355679,0.00002400503,0.2299559,0.0002999731,0.00001850367,0.00008452809,0.0003897212,0.02794773,0.09447473,0.1303548,0.5127443,0.001350126],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2119825,0.000001520505,0.04942292,0.0002120734,0.0003687061,0.0003743005,0.00001757555,0.0001588,0.7374616],"genre_scores_gemma":[0.8753078,0.00002468664,0.02472195,0.000286064,0.00005900708,0.00004271,0.00001458762,0.00002006581,0.09952314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6633253,"threshold_uncertainty_score":0.9530875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.012707130087182,"score_gpt":0.2282134740186697,"score_spread":0.2155063439314877,"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."}}