{"id":"W4324143454","doi":"10.38007/nep.2023.040207","title":"Sensitivity of Natural Environment of Tourist Attractions Based on Fuzzy Comprehensive Evaluation","year":2023,"lang":"en","type":"article","venue":"Nature Environmental Protection","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Sensitivity (control systems); Tourism; Natural (archaeology); Fuzzy logic; Computer science; Environmental science; Business; Artificial intelligence; Geography; Engineering","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":[0.001174829,0.0004888747,0.0003856235,0.003235499,0.0004972467,0.001185876,0.0002734405,0.0003541401,0.001105149],"category_scores_gemma":[0.002980869,0.0001548115,0.0007123405,0.001677641,0.0003939222,0.000714844,0.0007309686,0.0002151417,0.00005563151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001303827,"about_ca_system_score_gemma":0.0004845927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00984602,"about_ca_topic_score_gemma":0.006724206,"domain_scores_codex":[0.9990709,0.0001889216,0.00005813317,0.0001032944,0.000478048,0.0001007281],"domain_scores_gemma":[0.9992071,0.0003029716,0.0001027939,0.00003792221,0.000282731,0.00006643875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004468627,0.0002053311,0.1622378,0.0004568,0.0008396331,0.0009651352,0.001736861,0.6573675,0.02857967,0.01444339,0.001717364,0.1310036],"study_design_scores_gemma":[0.00001655797,0.0002976227,0.1684038,0.000049002,0.0001805182,0.0003278812,0.001375191,0.8110021,0.006475378,0.00992451,0.0018006,0.0001468769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8752801,0.0003432318,0.112941,0.0001393017,0.00002192162,0.0001151511,0.0002837511,0.0000894824,0.01078612],"genre_scores_gemma":[0.995618,0.00005903032,0.003873427,0.000005494515,0.00000309334,0.00002004359,0.00006746324,0.000002537356,0.0003509757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00984602,"threshold_uncertainty_score":0.01957744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149398585612766,"score_gpt":0.2445788382553286,"score_spread":0.2230848523992009,"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."}}