{"id":"W1970220953","doi":"10.1115/imece2007-42448","title":"On Line-Affective State Monitoring Device Design","year":2007,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Boredom; Computer science; Surprise; Human–computer interaction; Context (archaeology); Robot; State (computer science); Affective computing; Human–robot interaction; Experience sampling method; Artificial intelligence; Psychology; Social 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":[0.001167704,0.001331291,0.0008912642,0.0007210078,0.0003322278,0.001563743,0.002595816,0.00179077,0.01745828],"category_scores_gemma":[0.003530029,0.0006193276,0.0005243042,0.0003630343,0.0003466569,0.001153219,0.001178239,0.0006522584,0.004696736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003223947,"about_ca_system_score_gemma":0.0004039865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003099966,"about_ca_topic_score_gemma":0.0003760122,"domain_scores_codex":[0.9982849,0.0004370553,0.0001500121,0.0005639918,0.0004504454,0.000113526],"domain_scores_gemma":[0.9978492,0.0007318972,0.00020572,0.0004074275,0.0006579203,0.0001478081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003464552,0.001240561,0.00760398,0.002046137,0.0001379417,0.001033954,0.0009316543,0.002019146,0.5037937,0.003115401,0.01570433,0.4589086],"study_design_scores_gemma":[0.001324609,0.01113106,0.05670495,0.0005070124,0.0006278736,0.007577972,0.0006691645,0.1034056,0.6453851,0.004953277,0.1671531,0.0005602961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08491747,0.0005633865,0.8832893,0.0007183717,0.0008364436,0.004843243,0.0017166,0.007425616,0.01568956],"genre_scores_gemma":[0.4632609,0.0006590153,0.4846753,0.002468465,0.0003956485,0.008761166,0.001695224,0.000876182,0.03720805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01745828,"threshold_uncertainty_score":0.05840379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028199220687086,"score_gpt":0.3899605196239644,"score_spread":0.2871405975552559,"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."}}