{"id":"W2083637888","doi":"10.5367/000000008785633532","title":"Comparing Recreation Benefits from On-Site versus Household Surveys in Count Data Travel Cost Demand Models with Overdispersion","year":2008,"lang":"en","type":"article","venue":"Tourism Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Overdispersion; Count data; Negative binomial distribution; Econometrics; Tourism; Visitor pattern; Survey data collection; Estimator; Recreation; Truncation (statistics); Statistics; Poisson distribution; Sample (material); Economics; Geography; Mathematics; Computer science","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":[0.02652811,0.0008127696,0.001414041,0.001222438,0.0004192023,0.001912821,0.002104584,0.001641258,0.002685311],"category_scores_gemma":[0.07285323,0.0007941838,0.00178455,0.001664757,0.001220168,0.002613187,0.002011663,0.001610909,0.0003272887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001899739,"about_ca_system_score_gemma":0.0009225243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01363853,"about_ca_topic_score_gemma":0.008724608,"domain_scores_codex":[0.9873416,0.01074727,0.0002438209,0.0005667162,0.0006603776,0.0004401888],"domain_scores_gemma":[0.8419147,0.1442049,0.005991742,0.004959306,0.002274374,0.0006549887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003451766,0.001338036,0.1789955,0.0003431868,0.001162388,0.00031732,0.0007044285,0.7392724,0.0004916593,0.01777528,0.001451606,0.05469641],"study_design_scores_gemma":[0.0001558703,0.0008927184,0.03124431,0.0000275033,0.0003324244,0.00007273287,0.000430463,0.956633,0.000446144,0.009022415,0.0006784439,0.00006392599],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426557,0.0002704087,0.05386347,0.0005858855,0.00004740458,0.0001570725,0.0003566343,0.00007079899,0.001992648],"genre_scores_gemma":[0.9847463,0.0001595468,0.01267489,0.0001007008,0.0000376829,0.0001161964,0.0004335603,0.00002925727,0.00170183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02652811,"threshold_uncertainty_score":0.1402957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3244668774829773,"score_gpt":0.2270839975301864,"score_spread":0.09738287995279082,"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."}}