{"id":"W3135913138","doi":"10.20383/102.0324","title":"Expenditures of CARL member libraries for scholarly resource subscriptions licensed through CRKN for 2019 - 2020 / Dépenses des bibliothèques membres de l'ABRC pour les abonnements aux ressources savantes sous licence du RCDR pour l'année 2019 - 2020","year":2021,"lang":"fr","type":"article","venue":"Open MIND","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; Library science; Political science; Documentation; Computer science; Law","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001237607,0.0006149656,0.0009156885,0.0004036516,0.001789017,0.01525946,0.003788131,0.000373502,0.00192588],"category_scores_gemma":[0.001163044,0.0005804475,0.000414288,0.001763046,0.0008585154,0.04995292,0.001314568,0.0003298416,0.000244446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008413518,"about_ca_system_score_gemma":0.002406995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120145,"about_ca_topic_score_gemma":0.0003004242,"domain_scores_codex":[0.9948591,0.0005153458,0.001503201,0.001150827,0.0008248881,0.001146665],"domain_scores_gemma":[0.9948001,0.001550467,0.001044463,0.001095443,0.001136568,0.0003729779],"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":[0.000348761,0.0005110524,0.01103546,0.0004757216,0.000321758,0.00004378209,0.08572795,0.0007196449,0.02295288,0.006586755,0.8640161,0.007260161],"study_design_scores_gemma":[0.001826914,0.0004640516,0.003869314,0.001237815,0.00008839444,0.0001679331,0.04419854,0.005766799,0.2049397,0.002531754,0.7340726,0.00083621],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.803417,0.0564716,0.06991678,0.04051482,0.002874417,0.004482545,0.003192912,0.0001061806,0.01902375],"genre_scores_gemma":[0.3796487,0.008000994,0.4225381,0.003766332,0.002929161,0.0005675634,0.0006714812,0.0001812255,0.1816965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4237683,"threshold_uncertainty_score":0.9996647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033264854853481,"score_gpt":0.3258665598689355,"score_spread":0.2225400743835873,"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."}}