{"id":"W7075631307","doi":"","title":"Generating measures of access to employment for Canada's eight largest urban regions","year":2018,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Measure (data warehouse); Neighbourhood (mathematics); Key (lock); Code (set theory); Geocoding; Data access; Data collection","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":[0.0006734491,0.001072688,0.0006932615,0.005370084,0.00145681,0.001936524,0.001773406,0.0006690999,0.01139078],"category_scores_gemma":[0.00723287,0.0005130287,0.0008981541,0.01068471,0.0005498289,0.0004269681,0.001907999,0.001132113,0.005834199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0124828,"about_ca_system_score_gemma":0.02602634,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9660207,"about_ca_topic_score_gemma":0.9796566,"domain_scores_codex":[0.9987669,0.00008168022,0.00005970885,0.0002415058,0.0005469469,0.0003033475],"domain_scores_gemma":[0.9967754,0.0003802612,0.0002256963,0.0004082115,0.001795554,0.0004148753],"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":[0.0002255977,0.0001556215,0.1399575,0.0004826309,0.0001798419,0.0002679482,0.0007265923,0.01221526,0.0005789635,0.005729867,0.7985645,0.04091558],"study_design_scores_gemma":[0.0002325532,0.0000397572,0.3185076,0.0004595976,0.00007508952,0.0001969857,0.001587654,0.01904142,0.002049348,0.004061092,0.6535206,0.000228258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01729865,0.0001462751,0.00141284,0.0002693333,0.00002632652,0.0001035751,0.9751184,0.0009263512,0.004698302],"genre_scores_gemma":[0.0323915,0.0002129653,0.006394234,0.0001066347,0.00001319326,0.0003153844,0.9563636,0.0002365919,0.003965956],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0339793,"threshold_uncertainty_score":0.09056956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04267645297998614,"score_gpt":0.2957522583385263,"score_spread":0.2530758053585401,"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."}}