{"id":"W4230325026","doi":"10.32920/ryerson.14639367.v1","title":"ORCID IDs in the open knowledge era","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metadata; Identifier; World Wide Web; Computer science; Interoperability; Unique identifier; Outreach; Library science; Political science; Programming language","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","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.01733112,0.0001746028,0.0004149542,0.0001913478,0.00009169287,0.005029863,0.01004412,0.0001293212,0.006035502],"category_scores_gemma":[0.001802891,0.0000943072,0.0001273663,0.0007108102,0.00006589555,0.0003823977,0.02109605,0.000558453,0.001310003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003917883,"about_ca_system_score_gemma":0.0003648667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001548358,"about_ca_topic_score_gemma":0.01397757,"domain_scores_codex":[0.9956581,0.001362674,0.0007755393,0.0009196111,0.001064857,0.0002191901],"domain_scores_gemma":[0.9955326,0.001219624,0.0001664728,0.00286925,0.0001634073,0.00004867627],"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":[0.00000912354,0.0003230534,0.000267378,0.00002879354,0.00002619621,0.00004523987,0.006031595,0.00007959077,0.00000232898,0.2212729,0.6413461,0.1305676],"study_design_scores_gemma":[0.0002028359,0.00001337786,0.00811844,0.00005886256,0.00001106448,0.000001496,0.01625475,0.0003902233,0.00002524783,0.137538,0.8371696,0.0002160543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004848192,0.0003298872,0.009261956,0.01421828,0.001386233,0.0009444757,0.00004040783,0.00002236646,0.9689482],"genre_scores_gemma":[0.7940205,0.0002866699,0.01022878,0.0152557,0.0003318997,0.0004318249,0.0003567788,0.00002348184,0.1790643],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7898839,"threshold_uncertainty_score":0.9994676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.530129337104347,"score_gpt":0.55318074981532,"score_spread":0.023051412710973,"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."}}