{"id":"W4288060570","doi":"10.18357/kula.234","title":"Knowledge Lost, Knowledge Gained","year":2022,"lang":"en","type":"article","venue":"KULA knowledge creation dissemination and preservation studies","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Ontario Council of University Libraries; University of Toronto","funders":"","keywords":"Computer science; World Wide Web; Metadata; Archivist; Context (archaeology); Data science; Library science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004109555,0.0002446137,0.0002619066,0.0003655362,0.001526218,0.0002329244,0.0001916632,0.00001514173,0.001118451],"category_scores_gemma":[0.000162929,0.0002325542,0.00009164636,0.0002280659,0.0003015748,0.0006581793,0.0004316041,0.0001397371,0.0001987726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193363,"about_ca_system_score_gemma":0.00003841305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007748672,"about_ca_topic_score_gemma":0.0001145471,"domain_scores_codex":[0.9985276,0.000185402,0.0004217972,0.0003946528,0.0002473919,0.0002231572],"domain_scores_gemma":[0.9985222,0.0005886133,0.0001614195,0.0001803524,0.000466461,0.00008090698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004013142,0.0005591232,0.0002665848,0.0001801504,0.0001546788,9.231724e-7,0.1428487,0.00001355063,0.00002520782,0.7319865,0.09095341,0.03297104],"study_design_scores_gemma":[0.0004423663,0.0001203459,0.01038959,0.00006633648,0.00006677702,0.00000239052,0.01427001,0.001721827,0.00004344257,0.02425121,0.9483225,0.000303148],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03539012,0.00448813,0.0001006671,0.001720787,0.0007069924,0.0004967413,0.0001172993,0.0001675239,0.9568117],"genre_scores_gemma":[0.6218961,0.0001761045,0.0000231823,0.00006295197,0.000278461,0.0003195101,0.0003906547,0.00001799353,0.376835],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8573691,"threshold_uncertainty_score":0.9997947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08202230126807764,"score_gpt":0.3304454973673931,"score_spread":0.2484231960993155,"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."}}