{"id":"W2562437438","doi":"10.6017/ital.v35i4.9601","title":"Editorial Board Thoughts: Metadata Training in Canadian Library Technician Programs","year":2016,"lang":"en","type":"paratext","venue":"Information Technology and Libraries","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Technician; Metadata; Library science; Computer science; Training (meteorology); World Wide Web; Political science; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0003381335,0.000367133,0.0004930065,0.003679304,0.000333839,0.003328404,0.002643839,0.001296067,0.0001050688],"category_scores_gemma":[0.00006554999,0.0002815618,0.00005878048,0.002333891,0.0004326169,0.09636137,0.0007377582,0.000646014,0.001326051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004938067,"about_ca_system_score_gemma":0.002780453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009173225,"about_ca_topic_score_gemma":0.0004566814,"domain_scores_codex":[0.9975018,0.00005172984,0.0009632114,0.000333719,0.0003702264,0.0007793044],"domain_scores_gemma":[0.9983924,0.00005632122,0.0004753488,0.000779328,0.00005200985,0.0002445731],"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":[0.000005361866,0.00000370997,0.000174816,0.0000653504,0.0000158666,0.000001763751,0.002796357,0.000001062648,6.957039e-7,0.2270666,0.6714386,0.09842978],"study_design_scores_gemma":[0.000262509,0.00007675817,0.00001673019,0.0002186038,0.000001582151,0.00001682611,0.001127299,0.0001621955,0.0002264968,0.01128376,0.9862265,0.0003807621],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0002507493,0.002858815,0.06278334,0.0431765,0.1786302,0.003022299,0.0007789052,0.003241209,0.705258],"genre_scores_gemma":[0.1460503,0.01340919,0.1782094,0.0427806,0.1873304,0.005929668,0.01623286,0.0005045656,0.409553],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3147878,"threshold_uncertainty_score":0.9999636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167431100892696,"score_gpt":0.2099156729512909,"score_spread":0.1982413619423639,"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."}}