{"id":"W2188713710","doi":"10.1109/iemcon.2015.7344512","title":"A biometric modality based on the seismocardiogram (SCG)","year":2015,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Biometrics; Principal component analysis; Robustness (evolution); Computer science; Pattern recognition (psychology); Dimensionality reduction; Feature extraction; Modality (human–computer interaction); Artificial intelligence; Accelerometer; Curse of dimensionality; Autocorrelation; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003710536,0.0001342316,0.0001163609,0.0002475016,0.00003574572,0.00005637223,0.0001917503,0.0000537772,0.00002876696],"category_scores_gemma":[0.0001383207,0.00008910528,0.00008224089,0.001350265,0.00002439349,0.00006333635,0.00002775451,0.0001300918,0.0002653717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001250635,"about_ca_system_score_gemma":0.0000176492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007174486,"about_ca_topic_score_gemma":0.000001019575,"domain_scores_codex":[0.9991736,0.00003684771,0.0001153441,0.0001317542,0.0003079871,0.0002344514],"domain_scores_gemma":[0.9992465,0.0001873524,0.00001078161,0.000376915,0.00004697755,0.0001315227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009350175,0.0004129159,0.08873519,0.0002257571,0.0005975866,0.0001477068,0.0005683207,0.6684137,0.06349426,0.006155198,0.09925546,0.07190043],"study_design_scores_gemma":[0.002877638,0.0006290004,0.01971485,0.0001172276,0.0000982955,0.00001363881,0.0006802038,0.4197892,0.4960397,0.008258926,0.04983662,0.001944654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4081589,0.0003199528,0.2676119,0.001412658,0.002743148,0.0008028247,0.00002066693,0.002358738,0.3165713],"genre_scores_gemma":[0.9984381,0.000002131947,0.001072656,0.0001643108,0.0001841638,0.00003083205,0.000001877198,0.00002840171,0.00007757386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5902792,"threshold_uncertainty_score":0.3633608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04456906763173521,"score_gpt":0.2295284930354982,"score_spread":0.184959425403763,"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."}}