{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001114912,0.0008104946,0.0006810676,0.001211673,0.000569433,0.0009064901,0.001811939,0.001483162,0.003947769],"category_scores_gemma":[0.002946023,0.0005743825,0.0005792262,0.001023851,0.0006113526,0.00141131,0.00140139,0.001192387,0.001885304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000687607,"about_ca_system_score_gemma":0.0007784431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005317884,"about_ca_topic_score_gemma":0.0007802269,"domain_scores_codex":[0.9981232,0.0002693298,0.0001047499,0.0006126596,0.0007931467,0.00009694329],"domain_scores_gemma":[0.9990909,0.0002445046,0.0001415605,0.0002053404,0.0002668386,0.0000508951],"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":[0.0004102555,0.0001671786,0.00100382,0.0002616616,0.00009313103,0.0003161948,0.0002312995,0.008368351,0.5002633,0.01409661,0.004271203,0.4705169],"study_design_scores_gemma":[0.0002361677,0.001100737,0.002583004,0.00006447678,0.0001494313,0.002128841,0.0001061862,0.4487657,0.489095,0.01086618,0.04471852,0.0001857082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008071315,0.0003277222,0.9871493,0.0002034646,0.0003736888,0.0001376595,0.00005599344,0.001425193,0.002255602],"genre_scores_gemma":[0.163177,0.0003401682,0.8282946,0.0002785695,0.0002113247,0.0003285038,0.0001174901,0.0002007713,0.007051399],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003947769,"threshold_uncertainty_score":0.0132066,"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."}}