{"id":"W2904673858","doi":"10.29173/iq944","title":"Digital curation after digital extraction for data sharing","year":2018,"lang":"en","type":"article","venue":"IASSIST Quarterly","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preparedness; Digital curation; Data curation; Digital preservation; Library science; Service (business); Digital library; World Wide Web; Political science; Public relations; Business; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01099941,0.001128235,0.001211372,0.007444066,0.005900031,0.01202025,0.002630902,0.002401225,0.2868717],"category_scores_gemma":[0.04543126,0.0009168708,0.001786696,0.008266898,0.003127549,0.01832683,0.01408709,0.003650606,0.167489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002680024,"about_ca_system_score_gemma":0.005929846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00412833,"about_ca_topic_score_gemma":0.006739654,"domain_scores_codex":[0.9886758,0.002423835,0.001258497,0.001192615,0.005681974,0.0007671738],"domain_scores_gemma":[0.9652284,0.008604422,0.002202349,0.01117712,0.01023539,0.002552293],"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":[0.00003882945,0.00001722898,0.0001908426,0.0003544193,0.000013026,0.00008148993,0.0007443046,0.00003948879,0.0006479289,0.007609477,0.8950654,0.09519749],"study_design_scores_gemma":[0.000002215752,0.000004944627,0.0001716286,0.00008824899,0.000001791391,0.00003795632,0.0001837032,0.00002421588,0.000187799,0.001650963,0.9976395,0.000006910429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004065561,0.02033499,0.1622151,0.1689356,0.1714643,0.00332325,0.03337328,0.02840229,0.4078855],"genre_scores_gemma":[0.02257214,0.0146507,0.09411341,0.02661286,0.02918961,0.001722009,0.03959514,0.02044555,0.7510986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9879798,"threshold_uncertainty_score":0.9596813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06557756504965585,"score_gpt":0.2688769650505414,"score_spread":0.2032994000008856,"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."}}