{"id":"W4410022133","doi":"10.1016/j.dajour.2025.100581","title":"A dual-phase framework for detecting authentic and computer-generated customer reviews using large language models","year":2025,"lang":"en","type":"article","venue":"Decision Analytics Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Metropolitan University","keywords":"Dual (grammatical number); Computer science; Phase (matter); Natural language processing; Artificial intelligence; Linguistics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00197187,0.0001892694,0.0004479283,0.000647013,0.0005643771,0.0008959594,0.000388903,0.00009615941,0.00002679727],"category_scores_gemma":[0.0002931344,0.0001543712,0.0002627107,0.001036919,0.00001854556,0.0003720329,0.0002511198,0.0003012264,0.000007271035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006075818,"about_ca_system_score_gemma":0.00006914409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000172002,"about_ca_topic_score_gemma":0.000002385832,"domain_scores_codex":[0.9980928,0.0001198288,0.000765462,0.0003654961,0.0003096296,0.0003467567],"domain_scores_gemma":[0.9984538,0.0004078863,0.0003776703,0.0003778302,0.0002218519,0.0001609878],"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":[0.0001039894,0.0005305983,0.0004474472,0.00008362012,0.0006639266,0.0001531949,0.004181765,0.02524953,0.001799581,0.1010844,0.006947341,0.8587546],"study_design_scores_gemma":[0.0009994791,0.00003877185,0.00001572667,0.0003854604,0.000114722,0.00005063431,0.0001219436,0.983215,0.0001810228,0.01213215,0.002586789,0.0001583417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05826177,0.0024708,0.9383673,0.000109015,0.0005991423,0.0001377499,0.000002476086,0.00002429449,0.00002746019],"genre_scores_gemma":[0.2934782,0.0003818218,0.7051082,0.0006270242,0.0002543553,0.000002469136,0.000002847136,0.00001248302,0.0001326408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9579654,"threshold_uncertainty_score":0.8639757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0690834967072373,"score_gpt":0.3941266481487731,"score_spread":0.3250431514415358,"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."}}