{"id":"W6982003640","doi":"","title":"Generating measures of access to employment for Canadaâs eight largest urban regions","year":2018,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Mineralogy and Gemology Studies","field":"Earth and Planetary Sciences","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":[0.0005478849,0.00105681,0.0006547902,0.005653579,0.001570958,0.002049866,0.001729089,0.0006874373,0.0115143],"category_scores_gemma":[0.005477232,0.0004908384,0.0009019098,0.01175551,0.0005902661,0.0005418316,0.002027212,0.001168058,0.0056865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01220893,"about_ca_system_score_gemma":0.02466417,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9706681,"about_ca_topic_score_gemma":0.9837549,"domain_scores_codex":[0.9989028,0.00005263485,0.00004342427,0.0001975627,0.0005325906,0.0002709184],"domain_scores_gemma":[0.9971045,0.0002343035,0.0001895509,0.0003210654,0.001770605,0.0003799928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002071584,0.000178172,0.1398796,0.0004800938,0.0001998982,0.0002817875,0.0009160479,0.01160676,0.000811719,0.006510759,0.7982282,0.04069991],"study_design_scores_gemma":[0.0001786298,0.00003348046,0.2829275,0.0003547528,0.00006398801,0.0001490888,0.001794578,0.01527571,0.00202577,0.003562653,0.6934165,0.0002172514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02105189,0.0001468146,0.001289825,0.0002485632,0.00002659351,0.00009066802,0.9715839,0.000915519,0.004646195],"genre_scores_gemma":[0.03160269,0.0001771467,0.00508065,0.00007955533,0.00001128012,0.0002335723,0.958759,0.0002252831,0.003830782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02933186,"threshold_uncertainty_score":0.08858234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04715700055687722,"score_gpt":0.2713046556935744,"score_spread":0.2241476551366972,"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."}}