{"id":"W4390920719","doi":"10.1016/j.trpro.2023.12.080","title":"Parking Survey Design Using Gamification","year":2024,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixed logit; Preference; Incentive; Survey data collection; Choice set; Nested logit; Travel behavior; Transport engineering; Revealed preference; Logit; Set (abstract data type); Discrete choice; Computer science; Survey methodology; Business; Logistic regression; Operations research; Marketing; Engineering; Economics; Econometrics; Microeconomics; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01791326,0.001131858,0.0008844332,0.003146446,0.0006972835,0.001348499,0.001382682,0.001371847,0.01771662],"category_scores_gemma":[0.03259541,0.000864848,0.0009393387,0.002828145,0.0007602206,0.001312353,0.00191297,0.001417371,0.003368028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000829422,"about_ca_system_score_gemma":0.001445218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007087836,"about_ca_topic_score_gemma":0.0009492551,"domain_scores_codex":[0.9743445,0.0213752,0.001092725,0.001102286,0.001328358,0.0007570531],"domain_scores_gemma":[0.9772464,0.01486626,0.001317672,0.00244374,0.00338345,0.0007424656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005010834,0.007750485,0.03682355,0.003870548,0.000403621,0.0004861913,0.005515478,0.05520341,0.005936335,0.09710658,0.02011676,0.7617762],"study_design_scores_gemma":[0.004868134,0.02616383,0.04041865,0.001693199,0.0005852501,0.000727231,0.005851075,0.52225,0.01564553,0.179083,0.2021724,0.0005416851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1114683,0.0003291113,0.7984493,0.0009197993,0.00035951,0.06690544,0.00344146,0.001976436,0.01615074],"genre_scores_gemma":[0.2631975,0.0003925802,0.5536193,0.0006158509,0.00008075979,0.1759946,0.001320403,0.0001063157,0.004672632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01791326,"threshold_uncertainty_score":0.09473556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3162879058204462,"score_gpt":0.4233649033378064,"score_spread":0.1070769975173602,"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."}}