{"id":"W2998126536","doi":"10.1109/lsens.2019.2962365","title":"Novel Method for Synchronization of Multiple Biosensors","year":2019,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; Mount Sinai Hospital; University of Toronto; University Health Network; University of Waterloo; Toronto Rehabilitation Institute; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; AGE-WELL","keywords":"Synchronization (alternating current); Computer science; Bottleneck; Millisecond; Real-time computing; SIGNAL (programming language); Beat (acoustics); Time synchronization; Bridging (networking); Algorithm; Computer hardware; Embedded system; Telecommunications; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001153881,0.0001433682,0.000186105,0.0001481834,0.00005387162,0.00001981197,0.0002056192,0.00004113041,0.00000794098],"category_scores_gemma":[0.0003583597,0.0001355561,0.00009732495,0.0003539216,0.00005665471,0.0001824138,0.00001728875,0.00008778488,0.00003053214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000232108,"about_ca_system_score_gemma":0.000009881611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007381948,"about_ca_topic_score_gemma":4.488248e-7,"domain_scores_codex":[0.9988049,0.00003607981,0.0002199431,0.0004164777,0.0002209515,0.0003016476],"domain_scores_gemma":[0.9989458,0.0006246031,0.000104857,0.0002493869,0.00002229982,0.0000530777],"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":[0.00001780316,0.00002036967,0.00006003002,0.00004020971,0.000001412201,0.000002077311,0.00006393928,0.04217988,0.9571462,0.00008466126,0.0001449929,0.0002384173],"study_design_scores_gemma":[0.0004210265,0.00004877033,0.00008254984,0.00001394209,0.000004764603,0.00002194839,0.000008191455,0.1751922,0.823362,0.000003779003,0.0007119483,0.0001288881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167737,0.000001442456,0.08082167,0.0007196457,0.001101476,0.0004363814,0.00003386541,0.00006766926,0.00004411571],"genre_scores_gemma":[0.9889425,0.000003674044,0.008439716,0.002189997,0.00006638297,0.000008052972,0.000001089013,0.00003205206,0.0003165597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1337842,"threshold_uncertainty_score":0.5527815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02895093516779714,"score_gpt":0.2714057586433326,"score_spread":0.2424548234755355,"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."}}