{"id":"W6969070879","doi":"10.5683/sp3/aos5li","title":"Rossport Ontario. 1:50,000. Map Sheet 042D13, ed. 1, 1967","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Digital mapping; Geographic information system; Aerial photography; Orthophoto; Government (linguistics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004853177,0.001942273,0.001404762,0.005412016,0.001487896,0.003161475,0.001822199,0.0006075875,0.186636],"category_scores_gemma":[0.002861858,0.00104901,0.0006740868,0.02176712,0.0005579072,0.001398392,0.001220899,0.001029987,0.1649598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007709139,"about_ca_system_score_gemma":0.01367562,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8312691,"about_ca_topic_score_gemma":0.9071906,"domain_scores_codex":[0.9992661,0.00003610972,0.00005982992,0.0001763622,0.0002990891,0.0001624647],"domain_scores_gemma":[0.9981578,0.0001363984,0.0001851948,0.000255001,0.001054456,0.0002111271],"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":[0.00001412556,0.000002718473,0.0004937293,0.0002643081,0.000006851036,0.00001097579,0.00003716733,0.00005081564,0.00003651383,0.0002298222,0.9964309,0.00242205],"study_design_scores_gemma":[0.000021583,0.000002089235,0.005398294,0.0001222919,0.000006705142,0.00001703514,0.00009835446,0.00003833049,0.00006356325,0.0001537775,0.9940667,0.00001130298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004581822,0.000046257,0.00002262722,0.0000208641,0.00001097262,0.000004056156,0.9977165,0.00008829799,0.002044616],"genre_scores_gemma":[0.0004438226,0.000150618,0.0001942107,0.00001856351,0.000005789955,0.00003476014,0.9931014,0.0001140359,0.005936848],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.186636,"threshold_uncertainty_score":0.6243595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721587832089378,"score_gpt":0.258770490952725,"score_spread":0.2415546126318313,"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."}}