{"id":"W4413367892","doi":"10.18280/mmep.120702","title":"A Comparative Study of Traditional and Deep Learning Approaches for Multiclass Classification of Tourism News","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Artificial intelligence; Multiclass classification; Computer science; Deep learning; Machine learning; Data science; Natural language processing; Geography; Support vector machine; Archaeology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004735919,0.00006640208,0.0002209035,0.00005454309,0.00009785182,0.00002753622,0.00004480709,0.00005115619,4.313424e-7],"category_scores_gemma":[0.0002235478,0.00006305431,0.00002418244,0.00009553901,0.00008478472,0.00005221885,0.00000836659,0.0000747594,5.892058e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001125817,"about_ca_system_score_gemma":0.00001333708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003522891,"about_ca_topic_score_gemma":0.00001137465,"domain_scores_codex":[0.9994028,0.00003883314,0.0002220802,0.0001144674,0.0001155428,0.0001063151],"domain_scores_gemma":[0.9989872,0.0008339271,0.00005573645,0.0000385358,0.00004469085,0.00003992131],"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":[0.00003474608,0.0008939222,0.0004716501,0.001585084,0.0001282332,1.477156e-7,0.1487414,0.4231053,0.0001711985,0.4161417,0.000007522233,0.008719182],"study_design_scores_gemma":[0.000281302,0.00009229429,0.0002034454,0.0001715573,0.00002976498,7.778003e-8,0.01803934,0.9550604,0.00001207481,0.02598338,0.00006059344,0.00006573925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6565562,0.0001070918,0.3400857,0.00006966886,0.00002712755,0.0003754859,0.000001235673,0.00002848537,0.002748883],"genre_scores_gemma":[0.9923159,0.00001794997,0.00749043,8.841299e-7,0.00001915522,0.00006158656,0.00000142997,0.000004624404,0.00008796998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5319552,"threshold_uncertainty_score":0.257128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282410977169664,"score_gpt":0.2875892327611498,"score_spread":0.1593481350441834,"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."}}