{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00004155814,0.0001186411,0.00008499512,0.00006021381,0.0002148806,0.002505641,0.000281404,0.000009716789,0.0001884751],"category_scores_gemma":[0.000008849452,0.0001041319,0.00005988418,0.00001770794,0.0001730609,0.007289052,0.00005001407,0.00003870431,0.0004919539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000161134,"about_ca_system_score_gemma":0.000009024062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007264561,"about_ca_topic_score_gemma":0.00008685538,"domain_scores_codex":[0.9991198,0.000002493621,0.0002015812,0.0003529179,0.0001465901,0.0001766397],"domain_scores_gemma":[0.9994496,0.00003937248,0.00006404669,0.0003384669,0.00005830541,0.00005026194],"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":[0.0001499903,0.0002652606,0.0001182462,0.00004112466,0.00007777924,0.000005733874,0.003561919,1.471354e-7,0.00000545071,0.3070994,0.01188461,0.6767903],"study_design_scores_gemma":[0.0003489576,0.0007998728,0.00464525,0.00004331292,0.0000308922,0.000003695249,0.0009749793,0.001727199,0.000003947371,0.1349368,0.85619,0.0002951344],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07116296,0.00001287649,0.02219355,0.00111205,0.00135769,0.0004533795,0.003240353,0.000221888,0.9002452],"genre_scores_gemma":[0.9816906,2.243523e-7,0.00009127919,0.0001311542,0.001733013,0.00005498597,0.001654975,0.00001707747,0.01462672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9105276,"threshold_uncertainty_score":0.9985299,"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."}}