{"id":"W7020884014","doi":"","title":"Measurement and processing of multimodal physiological signals in response to external stimuli by wearable devices and evaluation of parameters influencing data acquisition","year":2023,"lang":"it","type":"dissertation","venue":"Università Politecnica delle Marche (Università Politecnica delle Marche)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electronic surveillance; Domain (mathematical analysis)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001093687,0.0009122546,0.0007264242,0.0009948853,0.0002964278,0.001908937,0.0007314989,0.001081446,0.00791399],"category_scores_gemma":[0.002605121,0.0003713405,0.0005798634,0.0009232114,0.0005996344,0.001357904,0.001235781,0.0004634762,0.002843211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002656246,"about_ca_system_score_gemma":0.0003838827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005293735,"about_ca_topic_score_gemma":0.001002977,"domain_scores_codex":[0.999028,0.0002509836,0.0000676163,0.0003240906,0.0002623937,0.00006697082],"domain_scores_gemma":[0.9990767,0.000359913,0.0001655992,0.0001603021,0.0002004064,0.00003701547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006159127,0.000207097,0.01226816,0.001851761,0.0001868382,0.0004191929,0.0007723671,0.001507628,0.5458546,0.002799595,0.004452249,0.4290645],"study_design_scores_gemma":[0.0001542479,0.003806742,0.3624947,0.001190802,0.0007310847,0.006069405,0.002197969,0.04739279,0.4727634,0.01655835,0.08616579,0.0004747587],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3168885,0.009900541,0.6377645,0.001534601,0.000943162,0.0009816895,0.004305217,0.002739093,0.02494268],"genre_scores_gemma":[0.7246161,0.008658898,0.2423736,0.001357452,0.0008624684,0.001512391,0.001786797,0.0003968378,0.01843541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00791399,"threshold_uncertainty_score":0.02647489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1060531659096051,"score_gpt":0.3543960674239008,"score_spread":0.2483429015142957,"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."}}