{"id":"W4290546809","doi":"10.29173/cais1231","title":"Digital content reuse assessment","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Institute of Museum and Library Services","keywords":"Reuse; Digital content; Computer science; Set (abstract data type); Object (grammar); Metadata; Content (measure theory); Cultural heritage; World Wide Web; Engineering; Political science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02573161,0.0008415923,0.000757943,0.02706005,0.003288035,0.009343056,0.002227461,0.001209699,0.01011081],"category_scores_gemma":[0.07509105,0.0003204293,0.0009813467,0.01331352,0.002249086,0.007908021,0.009077927,0.001246489,0.00315083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00545695,"about_ca_system_score_gemma":0.01037921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01407401,"about_ca_topic_score_gemma":0.01892255,"domain_scores_codex":[0.9404802,0.01193206,0.003295824,0.001901441,0.04092509,0.001465462],"domain_scores_gemma":[0.8967707,0.01855552,0.007167073,0.01095033,0.06430739,0.002248988],"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":[0.0001540039,0.0003480272,0.03738948,0.0008794333,0.00008233784,0.000277547,0.006180232,0.002747645,0.004745642,0.06109063,0.01138406,0.8747209],"study_design_scores_gemma":[0.0000761492,0.0009867467,0.09555724,0.003282009,0.0003469535,0.001671209,0.02223582,0.02833245,0.05752953,0.08171716,0.7078702,0.0003944477],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1955416,0.00357359,0.2786148,0.003149956,0.0003814729,0.005161385,0.002367042,0.001971313,0.5092388],"genre_scores_gemma":[0.6228003,0.003552645,0.2887455,0.0006939696,0.0001055398,0.002114578,0.002976981,0.0004971757,0.07851324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02706005,"threshold_uncertainty_score":0.1360834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0490364346873881,"score_gpt":0.228993205768744,"score_spread":0.1799567710813559,"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."}}