{"id":"W2773168634","doi":"10.32920/ryerson.14638275.v1","title":"ORCID: Using API Calls to Assess Metadata Completeness","year":2021,"lang":"en","type":"article","venue":"","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metadata; Interoperability; Completeness (order theory); Computer science; World Wide Web; 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","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.03505411,0.001730552,0.001432663,0.0128816,0.00254103,0.01045041,0.002811311,0.002192118,0.008809644],"category_scores_gemma":[0.1784064,0.001013773,0.001097162,0.005415071,0.001434826,0.01398128,0.01076877,0.002735412,0.006826576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002321741,"about_ca_system_score_gemma":0.0046504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01143609,"about_ca_topic_score_gemma":0.00670134,"domain_scores_codex":[0.9504073,0.01086235,0.008483462,0.003074463,0.02458744,0.002585063],"domain_scores_gemma":[0.8329235,0.06441897,0.01476467,0.04552032,0.03880355,0.003568968],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007767248,0.001151381,0.2765787,0.002922706,0.0006942915,0.0007722157,0.008257345,0.00930606,0.02018631,0.06291,0.1136071,0.4958466],"study_design_scores_gemma":[0.0007117609,0.001794678,0.1212946,0.00147235,0.0006125458,0.001604096,0.007436815,0.3760471,0.1390118,0.07006444,0.278667,0.001282737],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2370402,0.0009510149,0.3699063,0.001636421,0.0007912512,0.002339312,0.02889472,0.3134123,0.04502838],"genre_scores_gemma":[0.6216039,0.000488705,0.2930013,0.0009754312,0.0001922641,0.001832065,0.04529193,0.02731066,0.009303694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9871184,"threshold_uncertainty_score":0.1853861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2281464184956896,"score_gpt":0.3222594818071802,"score_spread":0.09411306331149055,"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."}}