{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004082707,0.0001448452,0.0001452809,0.0006049791,0.0001375411,0.0002308888,0.0002259058,0.0001211842,0.00006758938],"category_scores_gemma":[0.0001466137,0.0001499835,0.00004197547,0.001690588,0.00007603077,0.0003309876,0.000003697078,0.0005536907,0.0002824097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002143251,"about_ca_system_score_gemma":0.0002293245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003216606,"about_ca_topic_score_gemma":0.0002987958,"domain_scores_codex":[0.9974813,0.0002651075,0.0003512287,0.0003425529,0.0009809665,0.0005787878],"domain_scores_gemma":[0.9986374,0.0006339691,0.00001198633,0.0002225878,0.0003545342,0.0001395175],"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":[0.0002322812,0.0001715128,0.118827,0.01209533,0.0006891688,0.0005223903,0.01947513,0.4057617,0.3355351,0.008427212,0.05418284,0.04408028],"study_design_scores_gemma":[0.0001997498,0.00004846133,0.2774859,0.00047381,0.00001189521,0.000004802863,0.000192675,0.7004654,0.01322733,0.000333534,0.007215641,0.0003408123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7074355,0.002745938,0.2846463,0.00005051952,0.0006936432,0.00144967,0.00007435872,0.002213553,0.0006904217],"genre_scores_gemma":[0.9973903,0.0001446955,0.001737962,0.000002154383,0.0001843434,0.0001834826,0.00009876635,0.0001175907,0.0001407312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3223078,"threshold_uncertainty_score":0.6116148,"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."}}