{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003262173,0.001237071,0.001431641,0.01842055,0.001731188,0.005221708,0.002580578,0.0007452118,0.2666377],"category_scores_gemma":[0.02733251,0.00104239,0.0007450883,0.0394712,0.0004820969,0.002461314,0.00219588,0.001496691,0.2395806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006875482,"about_ca_system_score_gemma":0.0228977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2850294,"about_ca_topic_score_gemma":0.2281181,"domain_scores_codex":[0.9929316,0.0004746453,0.000989008,0.0007585685,0.003712453,0.001133788],"domain_scores_gemma":[0.9536222,0.005710375,0.003117351,0.009348829,0.02594259,0.00225864],"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":[0.0000742278,0.00001471682,0.001500334,0.0001212908,0.000009833065,0.00001637995,0.00005520568,0.00011596,0.0001113232,0.000887291,0.9907963,0.00629707],"study_design_scores_gemma":[0.00004865839,0.00001361283,0.01647134,0.0001165623,0.00001392812,0.00004763719,0.0002391727,0.0003341177,0.00093306,0.0009147438,0.9808115,0.00005560647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004178861,0.00001773037,0.0002877358,0.00007627543,0.00002886694,0.00004797647,0.9910544,0.001168188,0.006901059],"genre_scores_gemma":[0.002183121,0.00006542268,0.000807364,0.0000446994,0.00003373287,0.0002752329,0.9848488,0.001325401,0.01041619],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2850294,"threshold_uncertainty_score":0.8919921,"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."}}