{"id":"W3124383541","doi":"","title":"Yokoso! Japan: Classifying Foreign Tourists to Japan for Market Segmentation","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Market segmentation; Segmentation; Business; International trade; Advertising; Artificial intelligence; Computer science; Marketing","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":[0.0003757213,0.0006192692,0.0002612237,0.003771285,0.001115803,0.001405479,0.0004036045,0.0004702996,0.005495985],"category_scores_gemma":[0.001104583,0.0002103238,0.0006937013,0.003040787,0.0002860885,0.0008655994,0.001346585,0.0003014926,0.001904908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006487039,"about_ca_system_score_gemma":0.001221075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0610199,"about_ca_topic_score_gemma":0.108399,"domain_scores_codex":[0.9997867,0.0000244643,0.00002554173,0.00003441762,0.00003436029,0.00009457424],"domain_scores_gemma":[0.9991295,0.00006317881,0.0001626437,0.0000435457,0.0002602538,0.0003408786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00043021,0.0000849577,0.9727093,0.00007689568,0.00007087212,0.0002789934,0.002291112,0.0001105656,0.001567482,0.0001313095,0.004502808,0.01774558],"study_design_scores_gemma":[0.00001351136,0.000103222,0.9791379,0.0000335332,0.000101835,0.0001359939,0.01585216,0.0008320675,0.0003540597,0.00007159228,0.003345524,0.00001864985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919947,0.0001357565,0.000235899,0.00009776177,0.00002578534,0.0000880412,0.002898935,0.00002783669,0.0044952],"genre_scores_gemma":[0.9841792,0.0001887854,0.002140209,0.00005716333,0.00002786476,0.0001410011,0.008602967,0.00002936248,0.004633463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0610199,"threshold_uncertainty_score":0.1213294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04739769144083257,"score_gpt":0.3559867301254959,"score_spread":0.3085890386846634,"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."}}