{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002781158,0.0007827142,0.001245638,0.0001118185,0.000246742,0.0001298557,0.001584619,0.0003744155,0.002088996],"category_scores_gemma":[0.0006205032,0.0005763939,0.0004478155,0.0003546431,0.000188744,0.0008369317,0.0006553013,0.00066992,0.00002258485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002619627,"about_ca_system_score_gemma":0.0002947008,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7436101,"about_ca_topic_score_gemma":0.5532322,"domain_scores_codex":[0.993561,0.0003564585,0.001992087,0.0007903443,0.002139162,0.00116093],"domain_scores_gemma":[0.9962623,0.0005407283,0.0005961843,0.001820019,0.0004461689,0.0003345787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001480006,0.0008267754,0.2552867,0.001333701,0.002614535,0.00002312513,0.2429767,0.008993981,0.4501129,0.002373122,0.005333992,0.02864452],"study_design_scores_gemma":[0.01498085,0.002179029,0.09749765,0.005807379,0.0006482871,0.0001702018,0.02523324,0.5271351,0.1344355,0.001447559,0.1848642,0.005601034],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9471869,0.003166765,0.0003378321,0.000227763,0.0007379966,0.001990393,0.00007123675,0.0001746535,0.04610641],"genre_scores_gemma":[0.9955501,0.0002660148,0.000404719,0.0002485451,0.0001816764,0.0002095303,0.00001002627,0.0001531933,0.002976201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5181411,"threshold_uncertainty_score":0.9996688,"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."}}