{"id":"W7103995802","doi":"10.5281/zenodo.17513978","title":"In A multimodal dataset for assessing emotion, stress, and emotional workload in interpersonal work scenario.","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"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":[0.0005669071,0.001430433,0.0007522388,0.001474339,0.0007299866,0.0008993565,0.001145447,0.001532314,0.005524149],"category_scores_gemma":[0.001736584,0.0001919751,0.0007846876,0.001176842,0.0002500804,0.000625142,0.001532824,0.0007746961,0.007359236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006772853,"about_ca_system_score_gemma":0.0004262063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008279934,"about_ca_topic_score_gemma":0.02124159,"domain_scores_codex":[0.9991769,0.0001711155,0.00008057067,0.0002542161,0.0002119178,0.0001053639],"domain_scores_gemma":[0.9993311,0.0001403935,0.00008290292,0.0001508814,0.0001719844,0.0001227003],"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":[0.001389407,0.001032281,0.03779805,0.00211999,0.0003395576,0.0009222996,0.0007461043,0.003442147,0.01279804,0.001110095,0.8315777,0.1067244],"study_design_scores_gemma":[0.0003780816,0.000893464,0.3325036,0.0006477125,0.000299319,0.00201539,0.002667788,0.02533635,0.01551578,0.003029224,0.6164238,0.0002894337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09051248,0.00152067,0.005832225,0.000716123,0.0006205128,0.0006586119,0.8860376,0.003300747,0.01080102],"genre_scores_gemma":[0.07260275,0.0003313227,0.008959738,0.0003447809,0.0001522444,0.001163323,0.9107889,0.0001158429,0.005541021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008279934,"threshold_uncertainty_score":0.01848012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.042252133589099,"score_gpt":0.324485892222183,"score_spread":0.282233758633084,"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."}}