{"id":"W6931929385","doi":"10.5683/sp3/xfawb8","title":"Kowkash Ontario. 1:50,000. Map Sheet 042L03, ed. 1, 1970","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Unemployment and Economic Growth","field":"Economics, Econometrics and Finance","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; 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":[0.0004182551,0.001450918,0.001160915,0.004541173,0.001257185,0.002781,0.001484464,0.0005428098,0.1516569],"category_scores_gemma":[0.002915905,0.001019765,0.0005413789,0.02035118,0.0004617458,0.001190808,0.001017674,0.0008796742,0.1419573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008741074,"about_ca_system_score_gemma":0.01407144,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8897761,"about_ca_topic_score_gemma":0.9288714,"domain_scores_codex":[0.99932,0.00003482815,0.00006277629,0.0001481437,0.0002691479,0.0001651145],"domain_scores_gemma":[0.998145,0.0001318744,0.0002005105,0.0002201361,0.001081676,0.0002208474],"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":[0.00002271635,0.000004211779,0.001066102,0.0003043204,0.000009180931,0.00001531245,0.00004825126,0.00007761864,0.00003693066,0.0003130212,0.9946074,0.003494848],"study_design_scores_gemma":[0.00002951343,0.000003435095,0.01484464,0.0001495367,0.000008820846,0.00002395752,0.0001533113,0.00007993598,0.00009129384,0.0001862577,0.9844155,0.00001379903],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001026596,0.00006502437,0.00002521318,0.00002579871,0.000008733458,0.0000047858,0.997528,0.00006210701,0.002177772],"genre_scores_gemma":[0.0008753982,0.0002302344,0.000167548,0.00002340119,0.000006325565,0.00004464205,0.9884627,0.0000841556,0.01010543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1516569,"threshold_uncertainty_score":0.507343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746584739118084,"score_gpt":0.2101230218775044,"score_spread":0.1826571744863236,"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."}}