{"id":"W2123730035","doi":"10.5430/jbgc.v5n2p9","title":"Applying learning algorithms to extract anxiety levels using the heart rate variability measure","year":2015,"lang":"en","type":"article","venue":"Journal of Biomedical Graphics and Computing","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Haar wavelet; Haar; Anxiety; Mathematics; Artificial intelligence; Reliability (semiconductor); Pattern recognition (psychology); Computer science; Statistics; Wavelet; Machine learning; Psychology; Wavelet transform; Discrete wavelet transform; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01289923,0.000156337,0.0005021832,0.0001588486,0.0002701421,0.00008332897,0.0001188079,0.0001431141,0.000008569914],"category_scores_gemma":[0.001670984,0.00009973553,0.0001751669,0.000512185,0.000182607,0.00008173387,0.00009844149,0.001033159,7.507914e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007862876,"about_ca_system_score_gemma":0.0005481847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002975272,"about_ca_topic_score_gemma":6.488019e-7,"domain_scores_codex":[0.9977203,0.0004783901,0.0007416434,0.0002227166,0.0005209674,0.0003159809],"domain_scores_gemma":[0.9979281,0.0005820221,0.0002402981,0.0001649375,0.000407134,0.0006774677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004144825,0.0008957082,0.05783385,0.0002789741,0.0006958313,0.000152587,0.005394112,0.002701705,0.1739615,0.0007806657,0.0005097479,0.7563809],"study_design_scores_gemma":[0.003800216,0.00120124,0.03618642,0.0007696578,0.0003326138,0.002245143,0.001707412,0.8193747,0.0005066538,0.001575694,0.1319041,0.0003961124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7702384,0.0002686932,0.2214926,0.007139341,0.0004403509,0.0003672256,0.000001944086,0.00002071668,0.00003069963],"genre_scores_gemma":[0.9884011,0.000009715191,0.009500154,0.001330597,0.0007380685,0.000001546917,6.542363e-7,0.00001231583,0.000005856724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.816673,"threshold_uncertainty_score":0.4488617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05843469462963814,"score_gpt":0.3140011173180666,"score_spread":0.2555664226884284,"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."}}