{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.1294014,0.000723747,0.0008872548,0.01902343,0.001836105,0.01419825,0.006539265,0.001944238,0.00448007],"category_scores_gemma":[0.2294383,0.000916343,0.0007609871,0.01637784,0.003710327,0.0122173,0.005731032,0.003036118,0.006297313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007247824,"about_ca_system_score_gemma":0.0167337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04584307,"about_ca_topic_score_gemma":0.02991821,"domain_scores_codex":[0.923168,0.02166851,0.008445433,0.005068277,0.03996057,0.001689137],"domain_scores_gemma":[0.7155093,0.05703865,0.03769915,0.0552082,0.1323205,0.002224199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001048885,0.00008988072,0.01439206,0.001697251,0.0000956,0.0001866771,0.004602876,0.0007407555,0.00243727,0.02578219,0.08611988,0.8637508],"study_design_scores_gemma":[0.00003755694,0.0001460367,0.01224027,0.003211014,0.0002102955,0.0004963834,0.00398602,0.001597743,0.01073271,0.0085814,0.9585784,0.0001821094],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09908574,0.1500605,0.3399474,0.08302187,0.01342515,0.003456787,0.006582207,0.0230276,0.2813926],"genre_scores_gemma":[0.3143701,0.1047348,0.4413966,0.01879442,0.004406166,0.001479695,0.01493504,0.005325803,0.09455729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9858018,"threshold_uncertainty_score":0.684348,"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."}}