{"id":"W2032379548","doi":"10.3727/108354203108750049","title":"DISAGGREGATING VISITOR FLOWS: THE EXAMPLE OF CHINA","year":2003,"lang":"en","type":"article","venue":"Tourism Analysis","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visitor pattern; China; Geography; Economic geography; Marketing; Business; Computer science; Archaeology","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":[0.0004000631,0.000285375,0.000331188,0.002496911,0.0006460035,0.0007846921,0.0004768053,0.0002733104,0.001528192],"category_scores_gemma":[0.00102857,0.00009601805,0.0004495095,0.006424723,0.0003775416,0.0004409063,0.0008187317,0.0003160161,0.00009435917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002153414,"about_ca_system_score_gemma":0.001482693,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5178228,"about_ca_topic_score_gemma":0.4943167,"domain_scores_codex":[0.9997974,0.00004654404,0.00001124522,0.00003127416,0.00004386563,0.0000697148],"domain_scores_gemma":[0.9995683,0.00008558846,0.00007242909,0.00005794719,0.0001534165,0.00006219174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003162113,0.000178821,0.7787983,0.0002569354,0.000395135,0.003571295,0.00442202,0.07863154,0.002169586,0.02306577,0.006046346,0.102148],"study_design_scores_gemma":[0.00002948848,0.00005294947,0.8737257,0.00003011569,0.0001316959,0.0002140096,0.002951685,0.1090173,0.0003772204,0.003862958,0.009565577,0.00004137161],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990163,0.000304254,0.001278638,0.0003315363,0.00000770598,0.0000413876,0.001435777,0.00005697675,0.006380829],"genre_scores_gemma":[0.9960718,0.0002794873,0.001301036,0.00002662032,0.000007957678,0.0000146533,0.00103566,0.000009730417,0.001253117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5178228,"threshold_uncertainty_score":0.9700336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0216979358263827,"score_gpt":0.3109431478658959,"score_spread":0.2892452120395132,"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."}}