{"id":"W6913095845","doi":"10.5683/sp3/gr35zg","title":"Long Point Ontario. 1:50,000. Map Sheet 040I09, ed. 4, 1973","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); Aerial photography; Point (geometry); Government (linguistics); Image (mathematics)","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":[0.0004862742,0.001883834,0.001345118,0.004876887,0.001556993,0.002987449,0.001865434,0.0006724336,0.141713],"category_scores_gemma":[0.003035478,0.001006069,0.0007345795,0.02056877,0.0005451571,0.001326843,0.001155618,0.001073846,0.1321393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009601895,"about_ca_system_score_gemma":0.01677369,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8729193,"about_ca_topic_score_gemma":0.9295415,"domain_scores_codex":[0.9992719,0.00003557703,0.0000573643,0.0001744761,0.0002921017,0.0001684857],"domain_scores_gemma":[0.9979883,0.0001415942,0.0001789531,0.0002458899,0.001233691,0.0002115198],"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":[0.00001457747,0.000003004087,0.0005891402,0.0002578332,0.000008002729,0.000009690297,0.00003004398,0.00006046347,0.0000342907,0.0002018561,0.9966462,0.00214481],"study_design_scores_gemma":[0.00003280255,0.000002761515,0.008262116,0.0001618471,0.00001022057,0.00001937151,0.0001057549,0.000072202,0.00008521888,0.0002150882,0.9910173,0.00001525523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004694696,0.00004486974,0.00001975351,0.00002232878,0.000009471096,0.000003812717,0.9983292,0.00007392283,0.001449657],"genre_scores_gemma":[0.0004134494,0.0001224967,0.0001583531,0.00001930473,0.000005063134,0.00003247699,0.9953252,0.00008083419,0.003842769],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.141713,"threshold_uncertainty_score":0.4740772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638945454286157,"score_gpt":0.2556271684783403,"score_spread":0.2392377139354788,"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."}}