{"id":"W2959743065","doi":"10.1109/fg.2019.8756612","title":"Towards an EmoCog Model for Multimodal Empathy Prediction","year":2019,"lang":"en","type":"article","venue":"","topic":"Humor Studies and Applications","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Empathy; Support vector machine; Computer science; Valence (chemistry); Conversation; Test set; Artificial intelligence; Set (abstract data type); Concordance correlation coefficient; Negativity effect; Speech recognition; Pattern recognition (psychology); Machine learning; Psychology; Cognitive psychology; Mathematics; Statistics; Social psychology; Communication","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":[0.0006370525,0.0006000449,0.0003780062,0.0004182756,0.0001979846,0.0006251521,0.0005756179,0.0008012929,0.001167707],"category_scores_gemma":[0.001917879,0.000236437,0.0004267539,0.0002365777,0.0002011721,0.000577352,0.0005529346,0.001020599,0.0005268747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254827,"about_ca_system_score_gemma":0.0004930527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00323824,"about_ca_topic_score_gemma":0.003421626,"domain_scores_codex":[0.9997132,0.0001040851,0.00001107299,0.00009777643,0.00003859261,0.00003514146],"domain_scores_gemma":[0.9995328,0.0002184132,0.00005441677,0.00002877863,0.0001262218,0.00003933715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004724058,0.000520203,0.01700198,0.0001345348,0.0002358211,0.0002981786,0.0005438989,0.650974,0.03003427,0.01026358,0.005102073,0.2844191],"study_design_scores_gemma":[0.000003486394,0.00002753971,0.001116171,0.000006866587,0.000009584379,0.00001597842,0.00001283119,0.996569,0.0005011048,0.001379914,0.0003526029,0.000004921308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1547628,0.0007051372,0.8380572,0.0008875513,0.0001184434,0.00009456557,0.0002968319,0.001172651,0.003904807],"genre_scores_gemma":[0.914806,0.0002632276,0.08114286,0.0002427873,0.00007715054,0.0001815565,0.0003110589,0.00004907374,0.002926286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00323824,"threshold_uncertainty_score":0.006438792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04165611223157654,"score_gpt":0.3611571981376995,"score_spread":0.319501085906123,"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."}}