{"id":"W1978986669","doi":"10.1145/1978942.1979046","title":"Identifying emotional states using keystroke dynamics","year":2011,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":361,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Keystroke dynamics; Sadness; Keystroke logging; Computer science; Anger; Context (archaeology); Field (mathematics); Human–computer interaction; Artificial intelligence; Affective computing; Machine learning; Speech recognition; Password; Psychology; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004328458,0.0006178899,0.0005227936,0.001481186,0.0002327858,0.001094505,0.0002483307,0.0006170439,0.002066105],"category_scores_gemma":[0.003147672,0.0001758958,0.0003249382,0.0005699318,0.0002009011,0.001179654,0.0005075891,0.0004224592,0.001225686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001828878,"about_ca_system_score_gemma":0.00009055319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006399802,"about_ca_topic_score_gemma":0.0008929754,"domain_scores_codex":[0.9994606,0.00009224247,0.00005595664,0.0001678913,0.0001493068,0.00007398882],"domain_scores_gemma":[0.9987919,0.0005251698,0.0002114359,0.0001068853,0.0002859643,0.00007868653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001096259,0.0001811837,0.1140129,0.0004544172,0.0001731955,0.0004380611,0.001096112,0.00439871,0.3449315,0.00085186,0.002361681,0.5300042],"study_design_scores_gemma":[0.00007810711,0.00101767,0.5770204,0.0001789814,0.0002318879,0.002403657,0.001572896,0.2289229,0.1773238,0.004430117,0.006590005,0.0002296694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8409888,0.0008590446,0.1480893,0.00017985,0.0001191856,0.0002131146,0.001123406,0.001646903,0.006780386],"genre_scores_gemma":[0.9734495,0.0003735259,0.02430284,0.00003610467,0.00002630366,0.00006575428,0.0004918383,0.0000345789,0.001219351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002066105,"threshold_uncertainty_score":0.006911814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1347024491870148,"score_gpt":0.3490898049847891,"score_spread":0.2143873557977743,"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."}}