{"id":"W2603904625","doi":"10.15353/vsnl.v2i1.111","title":"Co-integrating thermal and hemodynamic imaging for physiological monitoring","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Infrared; Popularity; Biomedical engineering; Computer science; Hemodynamics; Thermal; Medicine; Cardiology; Optics; Psychology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0006131486,0.0005975671,0.0004485033,0.0004722463,0.0001327048,0.0007014861,0.0005209126,0.0007189959,0.001223438],"category_scores_gemma":[0.001337346,0.0003611486,0.0002712498,0.0002898075,0.0003009559,0.0009697214,0.001180154,0.0005170992,0.0003622361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000174303,"about_ca_system_score_gemma":0.0002468963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002181418,"about_ca_topic_score_gemma":0.00061851,"domain_scores_codex":[0.9994259,0.0001190323,0.00002783583,0.0001316874,0.0002400802,0.00005541441],"domain_scores_gemma":[0.9993817,0.0002782901,0.00009057213,0.00006686687,0.0001375201,0.0000451005],"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":[0.0002000186,0.0001300011,0.001787267,0.0001823096,0.00004456332,0.0001811393,0.00006929638,0.003307045,0.9144604,0.0003595472,0.0003582464,0.07892014],"study_design_scores_gemma":[0.00004474634,0.0009636728,0.01096021,0.00004600812,0.0001571422,0.001173822,0.0000752565,0.1847793,0.7951562,0.0009427905,0.005623982,0.00007691394],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3551607,0.004354382,0.6349689,0.0004287665,0.0002983992,0.0001824151,0.00008264559,0.001302128,0.003221652],"genre_scores_gemma":[0.8417824,0.001037391,0.1544359,0.0002508519,0.000159301,0.0001235063,0.00005786781,0.0001046566,0.002048358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001223438,"threshold_uncertainty_score":0.004092813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040593240146162,"score_gpt":0.26873867724759,"score_spread":0.2583327448461283,"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."}}