{"id":"W2988438918","doi":"10.22215/etd/2019-13618","title":"Non-Contact Bed-Based Monitoring of Vital Signs","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Vital signs; Computer science; SIGNAL (programming language); Artificial intelligence; Computer vision; Segmentation; Real-time computing; Medicine; Surgery","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008224448,0.0004695146,0.0005719961,0.0003113142,0.00003277183,0.00004277383,0.0003240222,0.0004121753,0.0000955159],"category_scores_gemma":[0.00002317347,0.0005059509,0.0002514618,0.0002285738,0.000008347099,0.0001885786,0.00001491472,0.0004196114,0.0001722535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001945668,"about_ca_system_score_gemma":0.00008819981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009564082,"about_ca_topic_score_gemma":0.00001469841,"domain_scores_codex":[0.9984173,0.00000958892,0.0004667336,0.0003143009,0.0004026287,0.0003894338],"domain_scores_gemma":[0.9990894,0.0001335637,0.0001216763,0.000424908,0.000129182,0.0001012842],"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":[0.00002528309,0.00002357242,0.01698476,0.001211684,0.0001245272,0.000009797473,0.0002034224,0.002413583,0.9779177,0.00001669619,0.0001023651,0.0009665524],"study_design_scores_gemma":[0.0003804148,0.0001203943,0.003263629,0.0007435526,0.00006151665,3.474078e-7,0.0006653689,0.0001861532,0.9939824,0.00001184933,0.00004420324,0.0005402196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9478083,0.0003701963,0.002161884,0.000001309882,0.009740262,0.0005596505,0.00003149635,0.0003711338,0.03895582],"genre_scores_gemma":[0.9964986,0.0000207849,0.001157948,0.000001624227,0.0005599115,0.00004878137,0.000216918,0.0001951311,0.001300329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04869032,"threshold_uncertainty_score":0.9997392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00995657171358071,"score_gpt":0.2363151299069378,"score_spread":0.226358558193357,"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."}}