{"id":"W4205562816","doi":"10.1109/access.2022.3143503","title":"PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Tourism; Attractiveness; Mobility model; Key (lock); Recommender system; Social media; Individual mobility; Process (computing); Fuzzy logic; Baseline (sea); Data mining; Artificial intelligence; World Wide Web; Machine learning; Geography; Computer security; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000316265,0.001086011,0.0004908768,0.00272045,0.0002981372,0.0005953097,0.0007220694,0.0005906657,0.001155643],"category_scores_gemma":[0.001264415,0.0002142635,0.0007859531,0.001568708,0.0001491567,0.001142942,0.0006705148,0.0005449574,0.0009935555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004242737,"about_ca_system_score_gemma":0.0004111726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02376408,"about_ca_topic_score_gemma":0.04267254,"domain_scores_codex":[0.999813,0.00003040168,0.00001500395,0.00006715397,0.00004244737,0.00003198551],"domain_scores_gemma":[0.999521,0.0001675425,0.00008276794,0.00006068011,0.0001050215,0.00006302288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00125291,0.0009105793,0.4680782,0.0007521681,0.0007879829,0.0007106643,0.0005821486,0.1159163,0.007080777,0.00229373,0.02514145,0.3764931],"study_design_scores_gemma":[0.00002691192,0.0002287893,0.08567791,0.00005670969,0.000136602,0.0003713433,0.0005686971,0.9022,0.002320599,0.002076232,0.006283288,0.00005291513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7844322,0.001867822,0.1628844,0.001080868,0.0002985046,0.0005358772,0.03743154,0.004644616,0.006824168],"genre_scores_gemma":[0.9244915,0.0006973912,0.05276322,0.00009722851,0.00009634858,0.0001872056,0.01883928,0.00005239985,0.002775503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02376408,"threshold_uncertainty_score":0.04725152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05062678771376346,"score_gpt":0.3421616949980683,"score_spread":0.2915349072843049,"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."}}