{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008199223,0.0007652649,0.0005352836,0.00218824,0.0003234312,0.001261219,0.0005238583,0.000454853,0.000960988],"category_scores_gemma":[0.001493017,0.0001425767,0.000614829,0.001742877,0.000312642,0.0009535057,0.0005415925,0.0003218238,0.0001556965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009640951,"about_ca_system_score_gemma":0.0005162275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008525866,"about_ca_topic_score_gemma":0.006733911,"domain_scores_codex":[0.999385,0.0001428386,0.0000555406,0.0001024414,0.0002457163,0.00006855935],"domain_scores_gemma":[0.9995616,0.000103011,0.0000580074,0.00001684349,0.0002291648,0.00003137859],"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":[0.0009695562,0.0005218139,0.1448247,0.0005557234,0.0006634805,0.0003956406,0.0004472672,0.4952618,0.01891472,0.004607046,0.003657854,0.3291804],"study_design_scores_gemma":[0.00001395855,0.0001715046,0.03608551,0.00002534959,0.00008122336,0.00004713345,0.0002177802,0.9587545,0.003069937,0.0008751488,0.000615883,0.00004208724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7744817,0.0008125934,0.2087324,0.0002500472,0.0001292116,0.0002447053,0.0005951587,0.0004505969,0.01430354],"genre_scores_gemma":[0.9862137,0.0002046538,0.01195142,0.00001437559,0.00001555291,0.00007644845,0.0002891299,0.00001064678,0.001223923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008525866,"threshold_uncertainty_score":0.01695251,"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."}}