{"id":"W2182592991","doi":"","title":"To amend City of Toronto Municipal Code Chapter 910, Parking Machines. The Council of the City of Toronto enacts: 1. Municipal Code Chapter 910, Parking Machines, is amended as follows: A. By inserting a new Subsection D in § 910-9 as follows:","year":2013,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Code (set theory); Parking lot; Code of practice; Business; Transport engineering; Engineering; Computer science; Civil engineering; Programming language; Construction engineering","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":[0.002589643,0.001301635,0.0009042727,0.001420437,0.01269507,0.007093456,0.003491475,0.01626811,0.08911017],"category_scores_gemma":[0.01343647,0.001830687,0.0009399747,0.00262505,0.00209172,0.002103214,0.002346482,0.008540809,0.05838454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04544433,"about_ca_system_score_gemma":0.1158436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9454201,"about_ca_topic_score_gemma":0.9794073,"domain_scores_codex":[0.9892538,0.0003586543,0.0004615268,0.0006944445,0.0061593,0.003072273],"domain_scores_gemma":[0.9809482,0.001421799,0.0005951927,0.001014354,0.01412827,0.001892193],"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":[0.00001766063,0.00001746342,0.0005124334,0.00005161236,0.000002779072,0.00005069534,0.0004421872,0.00006631584,0.0001645656,0.01230711,0.984236,0.002131195],"study_design_scores_gemma":[0.00002079381,0.00001374236,0.005135021,0.00007373952,0.00001348142,0.00002529423,0.0004724177,0.00008820584,0.0001542531,0.0005608274,0.993412,0.00003031634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.008448384,0.003453811,0.002697792,0.07086007,0.0184139,0.001479059,0.04263378,0.00163589,0.8503773],"genre_scores_gemma":[0.01877598,0.0007914337,0.001198042,0.03271674,0.0006926814,0.0005699625,0.006511454,0.0002777521,0.9384659],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.08911017,"threshold_uncertainty_score":0.3297232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429677063389583,"score_gpt":0.276594408183833,"score_spread":0.2336267018448747,"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."}}