{"id":"W6930626349","doi":"10.5281/zenodo.14186822","title":"Improving the Discovery of Restricted Data in Canada: Identifying Metadata Commonalities Across Restricted Data Sources","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Tribology and Lubrication Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Toronto Dementia Research Alliance; University of Toronto; University of Saskatchewan; Ontario Council of University Libraries; Canadian Respiratory Research Network","funders":"","keywords":"Metadata; Data element; Metadata repository; Data sharing; Data access; Geospatial metadata; Data dictionary; Data mapping; Meta Data Services","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005971495,0.0001189575,0.0001448116,0.0001430108,0.0004431875,0.0007154754,0.002782307,0.00004061994,0.0001530514],"category_scores_gemma":[0.000710187,0.000107636,0.00001397152,0.0009443705,0.00008713744,0.001343626,0.002655483,0.0003941705,0.00004605965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829228,"about_ca_system_score_gemma":0.00002975041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03983742,"about_ca_topic_score_gemma":0.006444409,"domain_scores_codex":[0.9985916,0.0001516536,0.0003403937,0.0003412793,0.000276474,0.0002985871],"domain_scores_gemma":[0.9982499,0.0001583313,0.00004639452,0.001425299,0.00007352014,0.00004657722],"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":[0.0001933898,0.0002205208,0.0009285684,0.004985075,0.001682668,0.0004169754,0.01075318,0.07972867,0.07649349,0.02040838,0.5148523,0.2893367],"study_design_scores_gemma":[0.0003611335,0.00002315601,0.02914454,0.0001808524,0.00005666935,0.00009285843,0.00279648,0.2165126,0.001074801,0.00004861383,0.7493593,0.0003490349],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8316327,0.01222764,0.124727,0.001266357,0.001147518,0.001024745,0.02176195,0.00262545,0.003586699],"genre_scores_gemma":[0.9920909,0.0002412358,0.0001059703,0.00001236966,0.00006667168,5.351907e-8,0.006976603,0.0003952989,0.0001108928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2889877,"threshold_uncertainty_score":0.9665564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06848892719412401,"score_gpt":0.260424176310339,"score_spread":0.191935249116215,"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."}}