{"id":"W6969184691","doi":"10.5683/sp3/y5ofry","title":"Cobden (West) Ontario. 1:50,000. Map Sheet 031F10, ed. 2, 1958","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Government (linguistics); Aerial photography; Digital mapping; Natural resource","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":[0.0004293026,0.001680192,0.001235304,0.004446045,0.001536009,0.003463804,0.001556857,0.0006243757,0.1826758],"category_scores_gemma":[0.002711947,0.0008622278,0.0006277936,0.01833292,0.0004650907,0.001171381,0.0009311918,0.001013854,0.138277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01241078,"about_ca_system_score_gemma":0.01801175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9562619,"about_ca_topic_score_gemma":0.97411,"domain_scores_codex":[0.9993,0.00002856703,0.00004657594,0.0001555845,0.0002813247,0.000187871],"domain_scores_gemma":[0.9978549,0.0001029357,0.0001428271,0.0002250211,0.001465216,0.0002090198],"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":[0.00001965876,0.000003177629,0.0006945759,0.0001843505,0.00000712909,0.00001065787,0.00002649256,0.00006306908,0.00002635811,0.0002593963,0.9949422,0.003762905],"study_design_scores_gemma":[0.00002690932,0.000002547649,0.009350225,0.0001401039,0.000007245356,0.00001717554,0.00009497026,0.00008064919,0.00008317007,0.0001471104,0.9900364,0.00001349221],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009446647,0.0000932464,0.00003040017,0.00003705087,0.00001602258,0.000006913882,0.995765,0.0001377194,0.003819219],"genre_scores_gemma":[0.001003702,0.000248025,0.0002152993,0.00003503199,0.000008341008,0.00004080411,0.9832991,0.0001495284,0.01500013],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1826758,"threshold_uncertainty_score":0.6111113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765046646453186,"score_gpt":0.2248787251494328,"score_spread":0.2072282586849009,"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."}}