{"id":"W2538736174","doi":"10.1063/1.4966095","title":"Determining factors affecting tourism demand for Malaysia using ARDL modeling: A case of Europe countries","year":2016,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Cointegration; Distributed lag; Order (exchange); Economics; Unit root; Exchange rate; Government (linguistics); Relative price; Gross domestic product; Quarter (Canadian coin); Business; Macroeconomics; Econometrics; Finance; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001041834,0.0006010451,0.0004854565,0.0009875981,0.000447257,0.001573269,0.0007621337,0.0009637893,0.001962678],"category_scores_gemma":[0.001814507,0.0004695728,0.001919003,0.001287543,0.0004241526,0.0008940469,0.0007403084,0.00115512,0.0003745916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171102,"about_ca_system_score_gemma":0.0009088165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09162667,"about_ca_topic_score_gemma":0.04536694,"domain_scores_codex":[0.9996566,0.0001315896,0.00002727482,0.00005912846,0.00003467573,0.00009079702],"domain_scores_gemma":[0.9984367,0.0009540856,0.0002048104,0.00008381563,0.000201028,0.0001194473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005543097,0.0008243519,0.5531932,0.0001989283,0.0003621767,0.006545795,0.0008562063,0.4205792,0.001663947,0.003368366,0.001855555,0.009997906],"study_design_scores_gemma":[0.0000428617,0.0002595056,0.1655787,0.00006198807,0.0001393877,0.0003035314,0.00328605,0.8274056,0.001006044,0.0006714655,0.001161461,0.00008342687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979392,0.0001112127,0.0007185894,0.0001101136,0.000005268525,0.00001024945,0.000265556,0.00001433687,0.0008254424],"genre_scores_gemma":[0.9984148,0.0001630031,0.0005209068,0.00001206877,0.000004539083,0.000009067156,0.0003720997,0.00000495116,0.0004985652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09162667,"threshold_uncertainty_score":0.1821867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0880800164502372,"score_gpt":0.3486585585767202,"score_spread":0.260578542126483,"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."}}