{"id":"W1965770187","doi":"10.1007/s10502-015-9243-7","title":"The ever changing face of digital curation: introduction to the special issue on digital curation","year":2015,"lang":"en","type":"article","venue":"Archives and Museum Informatics","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Digital curation; Data curation; Metadata; Digital preservation; Computer science; World Wide Web; Context (archaeology); Digital library; Data science; Digital asset management; Cultural heritage; Political science; Business; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006249198,0.00009646396,0.00007745534,0.00007494182,0.0003302638,0.0008182808,0.0001262683,0.000004823629,0.00001051149],"category_scores_gemma":[0.00003965933,0.00005321835,0.00003774561,0.0000387029,0.0001801385,0.002339473,0.0001005419,0.00006085301,0.00008247175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000628683,"about_ca_system_score_gemma":0.00001245646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001431455,"about_ca_topic_score_gemma":0.000007462498,"domain_scores_codex":[0.9993227,0.0000071786,0.0002571214,0.00006396862,0.0002094844,0.0001395744],"domain_scores_gemma":[0.9995925,0.00009251445,0.00009693141,0.0001335085,0.00002856219,0.00005599751],"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":[0.00005114412,0.00003360696,0.00000363917,0.0000176817,0.00002492205,1.719625e-7,0.08739278,0.0004456137,1.943171e-7,0.7356244,0.01858938,0.1578164],"study_design_scores_gemma":[0.0001262743,0.0002127466,0.0001233352,0.00001839523,0.000006339219,0.000002333841,0.01789934,0.001541701,0.000005071684,0.02623501,0.9537481,0.00008133628],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01912794,0.00001194671,0.004009796,0.02443631,0.001260669,0.000781747,0.0002762886,0.00004652959,0.9500487],"genre_scores_gemma":[0.9854655,0.00001995486,0.00004881873,0.000324172,0.006925046,0.00002345733,0.0001450295,0.000007767249,0.007040213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9663376,"threshold_uncertainty_score":0.7890701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02057666588589867,"score_gpt":0.2041461062232619,"score_spread":0.1835694403373632,"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."}}