{"id":"W3107336785","doi":"10.18280/ria.340513","title":"Competitiveness Evaluation of Tourist Attractions Based on Artificial Neural Network","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Artificial neural network; Backpropagation; Layer (electronics); Cluster analysis; Competition (biology); Core (optical fiber); Computer science; Index (typography); Quality (philosophy); Artificial intelligence; Market competition; Evaluation methods; Business; Engineering; Telecommunications; Geography; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002119329,0.0001223488,0.000208379,0.00008523952,0.0005435849,0.00008903998,0.0004290187,0.0000858257,0.004290289],"category_scores_gemma":[0.001377152,0.0001362387,0.0001361784,0.0008669036,0.0003786276,0.0001732102,0.00004927385,0.000256093,0.0005152012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160182,"about_ca_system_score_gemma":0.0002886012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004785736,"about_ca_topic_score_gemma":0.0002467363,"domain_scores_codex":[0.9971101,0.0006317743,0.0003720569,0.0003568038,0.001119835,0.0004093978],"domain_scores_gemma":[0.9982731,0.0005674189,0.0001593608,0.0002697011,0.0005183647,0.0002120925],"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":[0.0001017688,0.0002155751,0.0007975905,0.00002033934,0.00001353375,0.00001699132,0.001715142,0.9444415,0.0004884581,0.02039935,0.001280157,0.03050958],"study_design_scores_gemma":[0.00006860682,0.0002037507,0.0005933301,0.00006891222,0.00004477851,2.990609e-7,0.006485884,0.972528,0.008791686,0.001896425,0.009124239,0.00019403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4363956,0.0003133087,0.0365016,0.07103901,0.002624586,0.003337098,0.00007644624,0.0004016602,0.4493107],"genre_scores_gemma":[0.9983363,0.00001453738,0.0002558053,0.0002533295,0.0008483697,0.00002383604,0.00000938542,0.00001408314,0.0002443534],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5619407,"threshold_uncertainty_score":0.9966199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1823455495552105,"score_gpt":0.3893225686691841,"score_spread":0.2069770191139736,"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."}}