{"id":"W6894414670","doi":"10.5683/sp3/ihsvnp","title":"UNI-CEN Boundaries (CBF-Harmonized Shorelines) - Census Metropolitan Area (CMA) - 2001 - Esri Shapefile format (NAD83 CSRS / EPSG:3348)","year":2022,"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":[0.001515749,0.00183559,0.001370156,0.004556849,0.0009877834,0.002599543,0.003367859,0.001169521,0.0991964],"category_scores_gemma":[0.00614519,0.001273189,0.001008473,0.01266636,0.000370184,0.001907249,0.002362214,0.00209577,0.1539332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002725401,"about_ca_system_score_gemma":0.004448606,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1131032,"about_ca_topic_score_gemma":0.1593776,"domain_scores_codex":[0.998432,0.0002067203,0.0002019709,0.0003997906,0.0004611888,0.0002982934],"domain_scores_gemma":[0.9963761,0.00037445,0.0003394722,0.001045645,0.001576436,0.0002879257],"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":[0.0000232529,0.000008163133,0.0006920694,0.0002139773,0.0000113334,0.000009455584,0.00002888041,0.0000729126,0.0000733124,0.0004604844,0.9971524,0.00125363],"study_design_scores_gemma":[0.00006372254,0.000004826933,0.005297264,0.0001424863,0.000009555187,0.00002407921,0.0001100788,0.0001076808,0.0002285457,0.0004282091,0.993564,0.00001954703],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000715151,0.00001185233,0.00004981126,0.00002086632,0.00001164112,0.000007089989,0.9988316,0.0001749099,0.0008208781],"genre_scores_gemma":[0.0002234883,0.00001796258,0.0002233279,0.00001398658,0.000002474751,0.00005345989,0.9987016,0.00009035922,0.0006733485],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8868968,"threshold_uncertainty_score":0.331845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04778212231665309,"score_gpt":0.29534828684666,"score_spread":0.247566164530007,"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."}}