{"id":"W4390265790","doi":"10.3390/jrfm17010015","title":"Potential Integration of Metaverse, Non-Fungible Tokens and Sentiment Analysis in Quantitative Tourism Economic Analysis","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Dipartimenti di Eccellenza; Università degli Studi di Milano","keywords":"Tourism; Promotion (chess); Reputation; Sentiment analysis; Exhibition; Presentation (obstetrics); Digital economy; Computer science; Tourism geography; Destinations; Marketing; Business; Data science; World Wide Web; Political science; Geography; Artificial intelligence","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":[0.006887553,0.0004990313,0.0005548259,0.005395315,0.0004415125,0.003810827,0.0003945049,0.0005284461,0.004045268],"category_scores_gemma":[0.0239387,0.0001874591,0.0005459097,0.004927186,0.0007913871,0.004246715,0.001368415,0.0006965821,0.0005849899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009187572,"about_ca_system_score_gemma":0.00111526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00221443,"about_ca_topic_score_gemma":0.004295383,"domain_scores_codex":[0.9963171,0.002579028,0.0002535689,0.0002548497,0.0004581712,0.0001371781],"domain_scores_gemma":[0.9842266,0.01059089,0.002315583,0.001163964,0.001331135,0.0003718251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009180066,0.0005479013,0.2152966,0.001727757,0.0007838532,0.0007776075,0.00584762,0.03646598,0.006704796,0.2331486,0.007587888,0.4901935],"study_design_scores_gemma":[0.00006168052,0.0004036558,0.1310554,0.001557835,0.0002393384,0.0004413021,0.009374357,0.4257891,0.005013457,0.3764527,0.04937697,0.0002341011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4718489,0.004180766,0.4664942,0.005125897,0.0005736062,0.0007419551,0.005892972,0.0008909033,0.04425071],"genre_scores_gemma":[0.9092433,0.0006993814,0.08611903,0.0001423285,0.0001735019,0.0002482663,0.001185045,0.00007438315,0.002114825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006887553,"threshold_uncertainty_score":0.03642529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334449125970654,"score_gpt":0.3035960810525663,"score_spread":0.2902515897928598,"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."}}