{"id":"W6913307169","doi":"10.5683/sp3/ehxeci","title":"UNI-CEN Boundaries (CBF-Original Shorelines) - Census Metropolitan Area (CMA) - 1956 - Esri Shapefile format (WGS84 / EPSG:4326)","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Shapefile; North American Datum of 1927; File format; Census; Documentation; Geocoding; Boundary (topology); Metropolitan area","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012511,0.001784757,0.001287318,0.004890408,0.0009063273,0.002373703,0.002791078,0.000928822,0.09677421],"category_scores_gemma":[0.005245015,0.001089092,0.0008415077,0.01313167,0.0003991649,0.00168001,0.002130871,0.001786253,0.1507264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002731084,"about_ca_system_score_gemma":0.004246302,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1220233,"about_ca_topic_score_gemma":0.1616541,"domain_scores_codex":[0.9986122,0.000171167,0.0001692588,0.0003534063,0.0004256181,0.0002684004],"domain_scores_gemma":[0.9970711,0.0003061136,0.0002913706,0.0008220025,0.001243096,0.000266398],"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":[0.00002420512,0.000007762722,0.0008336915,0.0001949183,0.0000112579,0.00001011318,0.00003102135,0.00006608409,0.0000723665,0.0005421944,0.9968433,0.001363057],"study_design_scores_gemma":[0.00005254152,0.000004265247,0.00521704,0.0001158778,0.000007692609,0.00002179486,0.0001041057,0.00006587764,0.0001911942,0.0003927719,0.9938117,0.00001509191],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007823344,0.00001480623,0.00003989555,0.00001983635,0.00001303671,0.000006064233,0.9987831,0.0001422409,0.0009027589],"genre_scores_gemma":[0.0002621792,0.00002199403,0.0001782712,0.00001328381,0.000002942053,0.00003885913,0.9985949,0.00007236601,0.0008150495],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8779767,"threshold_uncertainty_score":0.323742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04305670092294162,"score_gpt":0.3080129073129131,"score_spread":0.2649562063899715,"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."}}