{"id":"W2605645340","doi":"10.1109/bhi.2017.7897293","title":"Towards unsupervised coherence-based assessment of ECG quality in different posture and movement conditions","year":2017,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Toronto","funders":"","keywords":"Computer science; Wearable computer; Coherence (philosophical gambling strategy); Artificial intelligence; Noise (video); SIGNAL (programming language); Wearable technology; Pattern recognition (psychology); Statistics; Mathematics; Embedded system","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001117374,0.0005429169,0.0005235117,0.00194846,0.0002002491,0.001029541,0.0004521432,0.0005784781,0.0006412346],"category_scores_gemma":[0.004009661,0.0001833201,0.0004281176,0.001119969,0.0003453347,0.0006735868,0.0007637532,0.0003816514,0.0004156203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865215,"about_ca_system_score_gemma":0.0003454779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001272357,"about_ca_topic_score_gemma":0.003200447,"domain_scores_codex":[0.9992498,0.0002272949,0.00006591648,0.0002236857,0.0001640122,0.00006929036],"domain_scores_gemma":[0.9982821,0.0006219129,0.0002969255,0.000157807,0.0005509231,0.00009043392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001622992,0.0006260488,0.1128987,0.0006045404,0.0004770046,0.0004714316,0.0008918901,0.0274685,0.2061364,0.001641717,0.002292946,0.6448678],"study_design_scores_gemma":[0.0001071908,0.001222569,0.4814105,0.0001176338,0.0003718718,0.001853035,0.0007127698,0.4598742,0.04784884,0.003554025,0.002789045,0.0001383381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4780009,0.0007960546,0.5163965,0.0001745441,0.0000458401,0.0002715073,0.0009166317,0.0007134757,0.002684699],"genre_scores_gemma":[0.8352663,0.000477209,0.1611903,0.00007432398,0.00007796079,0.0001997651,0.001511577,0.000110755,0.001091825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00194846,"threshold_uncertainty_score":0.005909324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05048694865129084,"score_gpt":0.4062408978325533,"score_spread":0.3557539491812625,"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."}}