{"id":"W2056558440","doi":"10.1007/978-3-642-33542-6_53","title":"The Empathy Machine","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Conversation; Empathy; Computer science; Visitor pattern; Interpersonal communication; Facial expression; Key (lock); Human–computer interaction; Component (thermodynamics); State (computer science); Multimedia; Artificial intelligence; Psychology; Communication; Social psychology; Computer security; Programming language","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":[0.0005200692,0.0005633574,0.0004264847,0.0006863879,0.0008048671,0.001742403,0.0007412667,0.001180705,0.02796085],"category_scores_gemma":[0.003048227,0.0002566526,0.0005003869,0.0005275476,0.001354133,0.003632977,0.002038134,0.001664075,0.00715709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004412283,"about_ca_system_score_gemma":0.0004259479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000531838,"about_ca_topic_score_gemma":0.0005470648,"domain_scores_codex":[0.9995726,0.0001430326,0.00001562186,0.0001427799,0.00008413223,0.00004189398],"domain_scores_gemma":[0.99942,0.0003184102,0.00002301469,0.0001473663,0.00005888008,0.00003235516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006274886,0.00004767171,0.0007136284,0.0001077998,0.00002222213,0.00006093369,0.0001704913,0.00417392,0.001213031,0.3868418,0.06427862,0.5423072],"study_design_scores_gemma":[0.00001383073,0.00003264416,0.0009985012,0.00009187765,0.0000234887,0.0002439167,0.0001354466,0.06975198,0.003145148,0.76257,0.1629704,0.00002285448],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01721747,0.008486863,0.7059058,0.0108285,0.001607475,0.0001069766,0.0008764464,0.003454708,0.2515158],"genre_scores_gemma":[0.4963102,0.0042297,0.2843484,0.002122937,0.001176408,0.0003535146,0.001913595,0.0007295029,0.2088156],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02796085,"threshold_uncertainty_score":0.0935384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848316883926198,"score_gpt":0.2926396977411957,"score_spread":0.2641565289019338,"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."}}