{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007242523,0.0002756859,0.0005015484,0.0002686084,0.0001610384,0.00641168,0.00451908,0.0001528395,0.0001196583],"category_scores_gemma":[0.00003821163,0.0002293182,0.0001363224,0.0008362103,0.00002881048,0.01045068,0.007718275,0.0002653006,0.0001457836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000067686,"about_ca_system_score_gemma":0.0007991596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000491288,"about_ca_topic_score_gemma":0.00001275322,"domain_scores_codex":[0.9971956,0.0001423339,0.0006844675,0.0007596249,0.0008267919,0.000391173],"domain_scores_gemma":[0.9970749,0.00006548325,0.0002453747,0.002074832,0.0002650732,0.0002743239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001996192,0.0005848177,0.009839267,0.002812447,0.0009849182,0.0005292737,0.04371959,0.2892566,0.04318976,0.434643,0.1046329,0.06978748],"study_design_scores_gemma":[0.0002728601,0.00004506445,0.002305987,0.0004734931,0.00001295278,0.0001354926,0.001907264,0.892635,0.0246508,0.0008399198,0.07503217,0.001688999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0264817,0.00005232458,0.9617254,0.0008850461,0.002713318,0.0003740293,0.00001962974,0.0002196155,0.007528936],"genre_scores_gemma":[0.4321034,0.00001224092,0.5543502,0.009984472,0.000305162,0.00004492819,0.0001514545,0.00002051395,0.003027651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6033784,"threshold_uncertainty_score":0.9946198,"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."}}