{"id":"W2800374350","doi":"10.1080/00036846.2018.1466988","title":"Investigating stationarity in tourist arrivals to India using panel KPSS with sharp drifts and smooth breaks","year":2018,"lang":"en","type":"article","venue":"Applied Economics","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Panel data; Economics; Hospitality; Econometrics; Structural break; Macroeconomics; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0007241389,0.0001090584,0.0001723439,0.0001500501,0.0003874722,0.0002458264,0.0002520635,0.0000807715,0.00009036324],"category_scores_gemma":[0.00009137988,0.0001220248,0.00001211326,0.0001954615,0.0004969563,0.0002553904,0.0001485192,0.000141691,0.00005507415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002105794,"about_ca_system_score_gemma":0.0003184922,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007475593,"about_ca_topic_score_gemma":0.00848725,"domain_scores_codex":[0.9988835,0.00003876345,0.0001900907,0.0003426174,0.0001411784,0.0004037998],"domain_scores_gemma":[0.9993212,0.0001277119,0.00009299431,0.000158409,0.00004626149,0.0002534316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002362567,0.0002515695,0.4132475,0.00007960878,0.0001527229,0.00004928784,0.1190603,0.003712553,0.00249945,0.4032861,0.002900527,0.05452424],"study_design_scores_gemma":[0.003712881,0.0003862385,0.7864563,0.0001611674,0.00004271177,0.00000549116,0.06109756,0.002906058,0.003701983,0.1192814,0.02025799,0.00199025],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502604,0.000004477472,0.00004522203,0.000613867,0.00004672949,0.0003832323,0.00001788638,0.00002665288,0.04860155],"genre_scores_gemma":[0.9894274,0.0000113069,0.009792555,0.0003741998,0.0002718857,0.00001520335,0.000003773192,0.00001682702,0.00008682024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3732089,"threshold_uncertainty_score":0.9991337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05899397310400008,"score_gpt":0.3168106395779882,"score_spread":0.2578166664739881,"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."}}