{"id":"W4293564569","doi":"10.1155/2022/9006925","title":"Decision-Making Research on Tour Lines of Tourist-Dedicated Train Based on Vague Sets and Prospect Theory","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Tourism; Operations research; Evidential reasoning approach; Line (geometry); Bounded rationality; Computer science; Decision model; Fuzzy set; Decision theory; Selection (genetic algorithm); Decision analysis; Business decision mapping; Product (mathematics); Rationality; Decision engineering; Decision-making; Index (typography); Decision support system; Fuzzy logic; Engineering; Data mining; Artificial intelligence; Operations management; Mathematics; Geography; Machine learning; Mathematical economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002632635,0.00008787425,0.0001903974,0.0004543597,0.0005123543,0.00001870774,0.000149823,0.0000525135,0.000142624],"category_scores_gemma":[0.0003132061,0.00008257015,0.00007301549,0.0006497083,0.000107321,0.0002038352,0.000001117928,0.000388146,3.517391e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008212226,"about_ca_system_score_gemma":0.0002448989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001675689,"about_ca_topic_score_gemma":0.0001599319,"domain_scores_codex":[0.9976357,0.0003355405,0.000519778,0.0001580024,0.001179408,0.0001715346],"domain_scores_gemma":[0.9978737,0.001130992,0.000435343,0.00008221666,0.0003992746,0.00007842284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002560389,0.0002366007,0.004476233,0.00002597898,0.00001846213,0.00005648587,0.01777924,0.9412243,0.0003731028,0.005420201,0.0001649851,0.02766399],"study_design_scores_gemma":[0.005855598,0.004490048,0.8835714,0.001409838,0.0001380106,0.000006350355,0.05453322,0.003296862,0.001058019,0.04026005,0.004869983,0.0005105979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889572,0.000117536,0.009157303,0.0006306998,0.0003310892,0.0002725099,0.00006024433,0.00002337535,0.000450078],"genre_scores_gemma":[0.9924889,0.00006187652,0.007204873,0.00008417855,0.00006837248,0.000009771702,0.00003008454,0.00001353248,0.00003846141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9379275,"threshold_uncertainty_score":0.3940666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02970886260975759,"score_gpt":0.3778559096778568,"score_spread":0.3481470470680992,"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."}}