{"id":"W2892693173","doi":"10.1109/jsen.2018.2871676","title":"An Approach to Optimize Multiple Design Objectives With Qualitative and Quantitative Criteria for a Wearable Body Sensor System","year":2018,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Color perception and design","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Computer science; Design methods; Reliability (semiconductor); Fuzzy logic; Control engineering; Reliability engineering; Engineering; Artificial intelligence; Embedded system","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":[0.006498393,0.002288549,0.001175929,0.002416668,0.0006310269,0.001897033,0.001110417,0.001256171,0.002473369],"category_scores_gemma":[0.007453599,0.0010212,0.001622773,0.001015751,0.000941128,0.001262737,0.001389547,0.001409305,0.0003484362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142135,"about_ca_system_score_gemma":0.002375824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001161211,"about_ca_topic_score_gemma":0.001524643,"domain_scores_codex":[0.9954997,0.001969728,0.0002809737,0.0004188907,0.001678466,0.0001521962],"domain_scores_gemma":[0.9974054,0.001390322,0.0003745322,0.0001385985,0.0006205507,0.00007062396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009331539,0.0001716546,0.0005688057,0.0006355773,0.000138903,0.00009585191,0.0002930893,0.8681147,0.01889359,0.02206291,0.0004583686,0.08847322],"study_design_scores_gemma":[0.00007342375,0.0006684785,0.0004378643,0.0001621262,0.0001234404,0.00009723699,0.0001700122,0.9609582,0.008452694,0.02287262,0.00593061,0.00005329067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003014912,0.00007681038,0.9950939,0.00004844813,0.000009633844,0.0001062795,0.00001134641,0.00004895611,0.001589609],"genre_scores_gemma":[0.0911748,0.0001656648,0.9063901,0.00006477028,0.00001813845,0.0006534244,0.00003772318,0.00006957696,0.001425826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006498393,"threshold_uncertainty_score":0.0343672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1253294729709811,"score_gpt":0.4173792604980397,"score_spread":0.2920497875270586,"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."}}