{"id":"W3202383385","doi":"10.18280/ts.380423","title":"Elderly Depression Recognition Based on Facial Micro-Expression Extraction","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Mental Health via Writing","field":"Psychology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Expression (computer science); Depression (economics); Hilbert–Huang transform; Feature extraction; Pattern recognition (psychology); Modal; Artificial intelligence; Feature (linguistics); Facial expression recognition; Computer science; Facial expression; Psychology; Facial recognition system; Computer vision","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003789753,0.0002029467,0.0001804721,0.0001213527,0.0002838358,0.00004534389,0.00009381265,0.0001759375,0.02230261],"category_scores_gemma":[0.00003171872,0.0002084961,0.00009735918,0.0001536359,0.00002016101,0.0001353348,0.00002161642,0.0003133824,0.0007229798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001365509,"about_ca_system_score_gemma":0.00006895256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003175434,"about_ca_topic_score_gemma":0.00001016185,"domain_scores_codex":[0.9977819,0.0003936554,0.0004640205,0.0005463818,0.0004025553,0.0004114673],"domain_scores_gemma":[0.9991091,0.0002003317,0.0001845275,0.0002284081,0.0000833335,0.0001943336],"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":[0.001018317,0.001318817,0.003790706,0.00009733524,0.00001867696,0.000189115,0.0005209391,0.00006943957,0.5571737,0.0000351549,0.00633878,0.429429],"study_design_scores_gemma":[0.009024843,0.001488086,0.07077925,0.001252219,0.00008236127,0.0001144779,0.00197052,0.00186675,0.8893738,0.0003930523,0.02281232,0.0008422613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702519,0.0001221869,0.00618923,0.0004618073,0.001160538,0.0005205711,0.00007251707,0.0001580713,0.02106323],"genre_scores_gemma":[0.9945519,0.000004262803,0.00234043,0.001598029,0.0005240202,0.0001452583,0.0004107071,0.00003378621,0.000391613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4285867,"threshold_uncertainty_score":0.9785911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05394439164276104,"score_gpt":0.3522490493351381,"score_spread":0.298304657692377,"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."}}