{"id":"W3171559874","doi":"10.21203/rs.2.16121/v1","title":"Geographic access to optometry services across Canada: Mapping distribution, need and self-reported use","year":2019,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Distribution (mathematics); Optometry; Geography; Regional science; Cartography; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007601699,0.0003471114,0.0003315611,0.003362722,0.001277912,0.001550852,0.0009829815,0.0003245695,0.00228819],"category_scores_gemma":[0.004073618,0.0002886444,0.000705335,0.01192732,0.000483784,0.0005180772,0.001295601,0.0005103547,0.000332865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02081329,"about_ca_system_score_gemma":0.04073618,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9983107,"about_ca_topic_score_gemma":0.9987234,"domain_scores_codex":[0.9991243,0.00007083851,0.00007155832,0.0001607382,0.0002922246,0.0002802744],"domain_scores_gemma":[0.9973124,0.0002726137,0.0003986967,0.0001055499,0.001458946,0.0004516783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005811532,0.00003274647,0.977521,0.0001042792,0.0001017787,0.00004098402,0.00143508,0.0007362951,0.0002315351,0.0004014545,0.005286895,0.01404978],"study_design_scores_gemma":[0.000006053904,0.00001007817,0.9933803,0.00005267356,0.00002842989,0.00002132668,0.002384946,0.001224549,0.00008761442,0.0000739198,0.002717634,0.0000124821],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9339154,0.0009641212,0.0008662659,0.000713446,0.00001282487,0.0001360833,0.05956827,0.00006830442,0.003755183],"genre_scores_gemma":[0.9800071,0.0007896882,0.002017413,0.0001114455,0.00000593928,0.00009533419,0.01378629,0.00001834912,0.003168293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02081329,"threshold_uncertainty_score":0.1510117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05755146087639947,"score_gpt":0.4198186910216948,"score_spread":0.3622672301452954,"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."}}