{"id":"W3191251748","doi":"10.1080/00987913.2021.1957076","title":"Making Up the Difference: Using Custom Reporting to Identify Metadata Inaccuracies in Link Resolver Serial Metadata","year":2021,"lang":"en","type":"article","venue":"Serials Review","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Library; University of Alberta","funders":"","keywords":"Resolver; Metadata; Computer science; World Wide Web; Database; Information retrieval; Telecommunications","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005745725,0.0002106249,0.0007306266,0.0001104577,0.0002590951,0.002889018,0.002120022,0.00005952419,0.00008112972],"category_scores_gemma":[0.003167915,0.0001385058,0.0001391684,0.002164032,0.00003668341,0.01270043,0.00171846,0.0001714099,0.00007344812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004822264,"about_ca_system_score_gemma":0.0004483413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007213905,"about_ca_topic_score_gemma":0.00002899272,"domain_scores_codex":[0.995437,0.000647821,0.002317295,0.0005428934,0.0006528734,0.000402104],"domain_scores_gemma":[0.9963223,0.0001616464,0.001456775,0.00178202,0.0001885489,0.00008868503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000667397,0.0001763493,0.005929605,0.01403418,0.0004531537,0.0008416825,0.02622507,0.0008549368,0.1814828,0.1106277,0.05121626,0.6080916],"study_design_scores_gemma":[0.0005806528,0.00005665496,0.01158933,0.01709836,0.00009778122,0.0007714534,0.001349102,0.004267032,0.02891035,0.001786976,0.9321036,0.001388676],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.276871,0.1621793,0.4591537,0.05903722,0.0298746,0.007321438,0.0001462569,0.0006903909,0.004726069],"genre_scores_gemma":[0.6355715,0.05681158,0.1845877,0.1040718,0.005664831,0.0006027088,0.0003693552,0.0001546794,0.01216574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8808874,"threshold_uncertainty_score":0.9981461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2727561724122823,"score_gpt":0.4290905812609487,"score_spread":0.1563344088486663,"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."}}