{"id":"W6893229623","doi":"10.5281/zenodo.15181219","title":"In A multimodal dataset for assessing emotion, stress, and emotional workload in interpersonal work scenario.","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Interpersonal communication; Emotional labor; Workload; Wearable computer; Affect (linguistics); Affective computing; Experience sampling method; Dynamics (music); Work (physics)","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":[0.0005733127,0.001403331,0.0007296156,0.0014854,0.0007296042,0.0008941461,0.001122452,0.001504765,0.005486545],"category_scores_gemma":[0.001746937,0.0001895773,0.0007777784,0.001171607,0.0002501231,0.0006222421,0.001509838,0.0007716957,0.007088319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006677798,"about_ca_system_score_gemma":0.000432147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008189359,"about_ca_topic_score_gemma":0.02096965,"domain_scores_codex":[0.9991837,0.0001715775,0.00007945533,0.0002511018,0.0002104531,0.0001036316],"domain_scores_gemma":[0.9993304,0.0001417953,0.00008347065,0.0001507108,0.0001718036,0.0001218473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001423203,0.001099826,0.04139135,0.002168211,0.0003614925,0.0009624325,0.0007950116,0.003660993,0.01391738,0.001160538,0.8196886,0.113371],"study_design_scores_gemma":[0.0003671339,0.0009063199,0.3482062,0.0006345811,0.0002990311,0.002030692,0.002744248,0.02602648,0.0159961,0.003047266,0.5994561,0.0002858541],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0977628,0.001529219,0.006342377,0.0007249559,0.0006375591,0.0006916703,0.8777855,0.003324263,0.01120166],"genre_scores_gemma":[0.07774079,0.0003335013,0.009586098,0.0003478378,0.000156328,0.001203079,0.9047487,0.0001194195,0.005764305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008189359,"threshold_uncertainty_score":0.01835436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04117308102428722,"score_gpt":0.3245253303416323,"score_spread":0.283352249317345,"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."}}