{"id":"W7043508933","doi":"","title":"Separation of Vibrational Cardiography signals by respiratory volume and phase using 1-dimensional Convolutional Neural Networks","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Convolutional neural network; Phase (matter); Pattern recognition (psychology); Artificial neural network; Volume (thermodynamics); Separation (statistics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005917401,0.0007454287,0.0008009924,0.0006939117,0.0006250761,0.00008014094,0.0002723383,0.0007587348,0.00006193098],"category_scores_gemma":[0.0001513034,0.0009322528,0.0003830582,0.0009145791,0.0001103646,0.0008935448,0.0000780503,0.00104014,0.00001892818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003812175,"about_ca_system_score_gemma":0.00005175071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007649757,"about_ca_topic_score_gemma":0.0000526793,"domain_scores_codex":[0.9963002,0.0002380205,0.001082337,0.0007752371,0.0009874022,0.0006168606],"domain_scores_gemma":[0.9981399,0.0003331825,0.0003974359,0.0003480025,0.0004736393,0.0003078513],"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":[0.0001554655,0.00007418026,0.0004685219,0.0002907744,0.0005007057,0.00002875385,0.000005096643,0.1149512,0.8785027,0.0006602763,0.0000970321,0.00426534],"study_design_scores_gemma":[0.007046067,0.0009100912,0.007106406,0.001839677,0.001335737,0.00008235463,0.0003077714,0.1705777,0.7958564,0.005720021,0.003805312,0.005412431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913495,0.002091008,0.00006057095,0.000001545312,0.002373055,0.0005801573,0.002482699,0.0003889955,0.0006725332],"genre_scores_gemma":[0.995752,0.00007356077,0.0006354595,0.00003430385,0.0002448477,0.00009719392,0.002759956,0.0002751002,0.0001275482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08264627,"threshold_uncertainty_score":0.9993128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968413147223093,"score_gpt":0.2625691752970496,"score_spread":0.2428850438248187,"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."}}