{"id":"W6999980367","doi":"","title":"Dynamic Parking Pricing using Transaction Data","year":2024,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Dynamic pricing; Parking guidance and information; Occupancy; Parking space; Database transaction; Transaction data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0008979475,0.0004249037,0.0004655491,0.0006906271,0.0002952114,0.001577928,0.00100805,0.0004834713,0.002423959],"category_scores_gemma":[0.004286956,0.0002929656,0.0003208808,0.001113269,0.0003385285,0.00258551,0.0007837658,0.0008038998,0.000495394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008114537,"about_ca_system_score_gemma":0.0006659342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00749725,"about_ca_topic_score_gemma":0.01047089,"domain_scores_codex":[0.9994462,0.0001803749,0.00003154232,0.0001370923,0.0001398395,0.00006491219],"domain_scores_gemma":[0.9983307,0.0008952596,0.0001402681,0.0001899086,0.0003483326,0.00009548364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003609108,0.0003498796,0.0246456,0.00008929209,0.00007870145,0.0002291157,0.000119079,0.7856903,0.001833578,0.01043189,0.004907626,0.171264],"study_design_scores_gemma":[0.000003143381,0.00001558657,0.0009060677,0.000002571417,0.000005045375,0.00001495476,0.00003225635,0.9952546,0.0002977328,0.003071288,0.0003918799,0.000004898435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6457953,0.0006161704,0.3334933,0.001788589,0.0002446567,0.0001767764,0.001703111,0.001451027,0.01473097],"genre_scores_gemma":[0.9870135,0.00009339927,0.01135985,0.00003363875,0.00002103534,0.00002498518,0.0004102666,0.00002344456,0.001019892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00749725,"threshold_uncertainty_score":0.01490724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733793229610312,"score_gpt":0.197900823522186,"score_spread":0.1705628912260829,"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."}}