{"id":"W2354064343","doi":"","title":"The correlation between inbound tourism development and economic growth in Tibet","year":2016,"lang":"en","type":"article","venue":"Ganhanqu ziyuan yu huanjing","topic":"China's Ethnic Minorities and Relations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Science North","funders":"","keywords":"Tourism; Economic geography; Correlation; Geography; Regional science; Natural resource economics; Economics; Mathematics; 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":[0.0004194349,0.0002271813,0.0001845169,0.001344946,0.0006599249,0.00109288,0.0003664334,0.0002638743,0.002763028],"category_scores_gemma":[0.0009760272,0.0001537671,0.0003700938,0.002190389,0.0006059682,0.000402872,0.0006538957,0.0006445156,0.0001908192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116127,"about_ca_system_score_gemma":0.001185332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09225153,"about_ca_topic_score_gemma":0.1614761,"domain_scores_codex":[0.9997925,0.00005217932,0.00002110655,0.00002455872,0.0000219852,0.00008778268],"domain_scores_gemma":[0.9989058,0.0002325207,0.0002281856,0.00003323716,0.000119666,0.000480607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006683868,0.00003118971,0.9969759,0.000009766351,0.00003969888,0.0001779715,0.0004327259,0.0001938684,0.0001665133,0.000187629,0.000114257,0.001603776],"study_design_scores_gemma":[0.000002434328,0.00002348558,0.9977181,0.000005746953,0.00001656786,0.00004339896,0.00137956,0.0005462134,0.0000248055,0.00007371642,0.0001620522,0.000003888212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988822,0.00006413203,0.00002033129,0.00005584096,0.000004232016,0.000001185113,0.00009490654,0.000001912488,0.0008750412],"genre_scores_gemma":[0.9995624,0.00002717987,0.000007265228,0.000006088664,0.000002830763,9.287128e-7,0.00007899786,5.749508e-7,0.0003135671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09225153,"threshold_uncertainty_score":0.1834291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298006221831769,"score_gpt":0.2768919918184434,"score_spread":0.2539119296001258,"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."}}