{"id":"W6989209291","doi":"","title":"Additional input from the Archives of Ontario","year":2020,"lang":"en","type":"article","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","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; Spring (device); Government (linguistics); Information system; Digital Archives","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":[],"category_scores_codex":[0.005239075,0.001217661,0.001205064,0.0102967,0.008090932,0.004570965,0.00179141,0.002208635,0.3876972],"category_scores_gemma":[0.02860582,0.0008676184,0.0008552167,0.01938247,0.001050221,0.001697671,0.003994328,0.001559811,0.07909664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04192178,"about_ca_system_score_gemma":0.09035995,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9421791,"about_ca_topic_score_gemma":0.9781331,"domain_scores_codex":[0.9892084,0.0006438346,0.0007774643,0.0008002873,0.006942581,0.001627452],"domain_scores_gemma":[0.9508917,0.003604364,0.0009024101,0.001828097,0.03970378,0.003069619],"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":[0.00007024083,0.0000127891,0.001494663,0.0004060081,0.000005618219,0.0001711071,0.001405272,0.0000414583,0.000288455,0.0006034046,0.982166,0.01333491],"study_design_scores_gemma":[0.00002373824,0.00000564881,0.007148853,0.0001776586,0.000009640156,0.00003843954,0.001548971,0.00002972825,0.0001230678,0.00009331134,0.9907833,0.00001747909],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.005435216,0.001451981,0.0009585634,0.02205999,0.003057242,0.001812689,0.6326706,0.0009264167,0.3316272],"genre_scores_gemma":[0.0240466,0.002756734,0.004160498,0.006220493,0.000904134,0.001857813,0.1688095,0.001220701,0.7900236],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3876972,"threshold_uncertainty_score":0.8733756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02256462175635884,"score_gpt":0.1782401055407359,"score_spread":0.1556754837843771,"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."}}