{"id":"W4285465244","doi":"10.32920/ryerson.14638275","title":"ORCID: Using API Calls to Assess Metadata Completeness","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metadata; Interoperability; Completeness (order theory); World Wide Web; Computer science; Publishing; Library science; Information retrieval; Political science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.03752048,0.001771579,0.001558888,0.01296388,0.00264235,0.01117645,0.003130117,0.00247027,0.008902788],"category_scores_gemma":[0.1901333,0.001076432,0.001134665,0.005842392,0.001616369,0.01522914,0.01177844,0.003018741,0.007128557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002492938,"about_ca_system_score_gemma":0.004888116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027515,"about_ca_topic_score_gemma":0.005986061,"domain_scores_codex":[0.9439312,0.01263561,0.009364434,0.00343175,0.02772303,0.002913983],"domain_scores_gemma":[0.8147922,0.07019857,0.01566088,0.05620445,0.03923616,0.003907695],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008198955,0.001111273,0.2571512,0.003043605,0.0007010724,0.0007924662,0.007854676,0.01028861,0.02083349,0.08453368,0.1117592,0.4937319],"study_design_scores_gemma":[0.0007240342,0.001625732,0.101239,0.001479362,0.0005932312,0.001572638,0.006495402,0.3602078,0.1378728,0.09372745,0.293256,0.001206607],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2198549,0.001075834,0.4056796,0.00168532,0.0008069926,0.002110243,0.02729968,0.2971536,0.04433374],"genre_scores_gemma":[0.6117574,0.0005576462,0.2998588,0.001035698,0.000217854,0.001764269,0.0451176,0.03030503,0.009385643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9888235,"threshold_uncertainty_score":0.1984296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2698310950701981,"score_gpt":0.3375788699897789,"score_spread":0.06774777491958078,"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."}}