{"id":"W4410846086","doi":"10.1007/978-3-031-93418-6_10","title":"Beyond Words: Exploring Emotional Contrasts Between Human and ChatGPT Responses in Medicine and Finance","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Saskatchewan","funders":"","keywords":"Computer science; Cognitive science; Artificial intelligence; Data science; Finance; Psychology","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":[0.0006608341,0.0001821914,0.0001577151,0.0003722058,0.0003470471,0.002022557,0.0002601882,0.0005858836,0.004014501],"category_scores_gemma":[0.009794063,0.0001141651,0.0001175443,0.0003854579,0.0007102276,0.001522143,0.001072145,0.000772115,0.0004782198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003640614,"about_ca_system_score_gemma":0.00010488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006238706,"about_ca_topic_score_gemma":0.001002772,"domain_scores_codex":[0.9995252,0.0002913093,0.00001052489,0.00004675904,0.00008513355,0.00004118089],"domain_scores_gemma":[0.9960299,0.003417226,0.0002124646,0.0000809941,0.000150312,0.0001091216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.005044166,0.0003157463,0.1440144,0.001586477,0.00025939,0.003915164,0.3662454,0.004126426,0.1970099,0.03866699,0.01100508,0.2278108],"study_design_scores_gemma":[0.00009758864,0.0006772457,0.7388048,0.0004015355,0.0001608527,0.003168279,0.1592837,0.01847301,0.01407108,0.03179842,0.03290223,0.0001612138],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697208,0.0004113879,0.006626664,0.0004412035,0.00007223395,0.00003650003,0.0002707179,0.0000599065,0.02236055],"genre_scores_gemma":[0.9952155,0.0001055741,0.001807738,0.0002198283,0.00002868894,0.00003240347,0.0001419567,0.00004680192,0.002401518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004014501,"threshold_uncertainty_score":0.01342982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.166796763417537,"score_gpt":0.3973771900728425,"score_spread":0.2305804266553055,"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."}}