{"id":"W2993813839","doi":"10.18280/ria.330403","title":"Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network","year":2019,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Echo (communications protocol); State (computer science); Echo state network; Flow network; Computer science; Flow (mathematics); Artificial intelligence; Geography; Computer security; Mathematics; Algorithm; Artificial neural network; Archaeology; Mathematical optimization; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003396112,0.0006697375,0.0004446728,0.0006607927,0.0002528561,0.0005817297,0.0005786254,0.0004659866,0.0007743536],"category_scores_gemma":[0.0008510561,0.0003590594,0.0005693278,0.0005173768,0.0002732002,0.0009226085,0.0005325292,0.0008458779,0.0001214953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007474622,"about_ca_system_score_gemma":0.0008288547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0366971,"about_ca_topic_score_gemma":0.02314432,"domain_scores_codex":[0.9998471,0.00001913186,0.00001033278,0.00004942299,0.00004106902,0.00003289368],"domain_scores_gemma":[0.9997707,0.00008596511,0.00002970914,0.00001253502,0.00008204531,0.00001907764],"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":[0.00006331979,0.00005331538,0.003869397,0.00003039227,0.00003432707,0.00005637825,0.00003264401,0.9535345,0.001537001,0.001117779,0.0007137941,0.03895717],"study_design_scores_gemma":[8.822175e-7,0.000003220325,0.0002245865,6.306902e-7,0.000001726433,0.000001122373,0.000001667054,0.9994949,0.000111924,0.0001348071,0.00002307199,0.000001378512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3370956,0.0008052386,0.6555787,0.0004244748,0.0001293197,0.00006405301,0.000472899,0.001058362,0.00437135],"genre_scores_gemma":[0.9759809,0.000354292,0.02101691,0.00003909193,0.00002451135,0.00004369825,0.0003947967,0.00001485365,0.002130835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0366971,"threshold_uncertainty_score":0.07296699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757204571305359,"score_gpt":0.2285891147133493,"score_spread":0.2110170690002957,"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."}}