{"id":"W4391617786","doi":"10.32920/25169618.v1","title":"A Service Area Analysis of Indoor Swimming Pools in Toronto","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Demographics; Service (business); Census; Population; Probabilistic logic; Geography; Business; Transport engineering; Statistics; Demography; Engineering; Mathematics; Marketing","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":[0.0002651062,0.000243917,0.0001850152,0.001488113,0.0008413718,0.0008963981,0.000460382,0.0001968146,0.003365859],"category_scores_gemma":[0.001673916,0.0001612073,0.0004632814,0.003525033,0.0003477834,0.0003105169,0.0006073331,0.0001480384,0.0003720071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01170781,"about_ca_system_score_gemma":0.006910586,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9468248,"about_ca_topic_score_gemma":0.9639239,"domain_scores_codex":[0.999681,0.00004505235,0.00001867513,0.00004781568,0.00009840874,0.0001090058],"domain_scores_gemma":[0.9994859,0.0000822563,0.00007422137,0.00003146428,0.0002663618,0.00005980782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000150628,0.00005348321,0.873489,0.0002123909,0.0001017537,0.0005221653,0.00443652,0.06591438,0.001679514,0.007390242,0.007441743,0.03860824],"study_design_scores_gemma":[0.000004559392,0.00003850418,0.9293054,0.00003830249,0.00002600505,0.00007558196,0.007008817,0.05761154,0.000333811,0.0003501463,0.005187153,0.00002011286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851834,0.0001313517,0.001430687,0.00009962223,0.000004484764,0.00004968243,0.005738391,0.00004079596,0.007321645],"genre_scores_gemma":[0.9930999,0.0001043018,0.001343967,0.00000910789,0.000001781453,0.00002350665,0.003676266,0.000008500197,0.001732728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05317515,"threshold_uncertainty_score":0.1069766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03510101315656732,"score_gpt":0.3482640041233439,"score_spread":0.3131629909667765,"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."}}