{"id":"W7097560434","doi":"","title":"IDENTIFIERS *National Archives DC; *National Library of Canada; State Archives","year":2016,"lang":"en","type":"article","venue":"","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"National archives; Government (linguistics); State (computer science); Identifier; Administration (probate law); National library; State government","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001263799,0.001417443,0.001377909,0.01000202,0.009115873,0.005998666,0.002549707,0.001666739,0.3775913],"category_scores_gemma":[0.01122632,0.00083589,0.0003461723,0.03408502,0.001195519,0.002680748,0.001907008,0.002353735,0.2614451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02717749,"about_ca_system_score_gemma":0.08155404,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.907909,"about_ca_topic_score_gemma":0.9265634,"domain_scores_codex":[0.9962863,0.0001789997,0.0003460452,0.0002832585,0.002397299,0.000508016],"domain_scores_gemma":[0.9734085,0.0007636127,0.001259283,0.0009007343,0.0221451,0.001522804],"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":[0.000006976654,0.000006408765,0.0004671001,0.000106652,0.000001656051,0.00002552652,0.00006880328,0.00001945229,0.00002217798,0.001132774,0.9895247,0.00861776],"study_design_scores_gemma":[0.000005007175,0.000003151394,0.002717919,0.0001577613,0.000004087673,0.00003510001,0.0003470661,0.00002650598,0.0000720189,0.0002101148,0.9964054,0.00001585858],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008130468,0.001260165,0.0005328574,0.004429864,0.003834501,0.0006947182,0.411618,0.0009312013,0.5758857],"genre_scores_gemma":[0.006533414,0.00410653,0.001997978,0.002854839,0.0005603863,0.001105788,0.1576939,0.0005813455,0.8245659],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.907909,"threshold_uncertainty_score":0.8877904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889436243949897,"score_gpt":0.1757852414509349,"score_spread":0.1568908790114359,"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."}}