{"id":"W4297894982","doi":"10.1364/sensors.2022.stu4c.3","title":"Development of a Wearable Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fiber and Polydimethylsiloxane","year":2022,"lang":"en","type":"article","venue":"Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital du Sacré-Cœur de Montréal; École de Technologie Supérieure","funders":"","keywords":"Materials science; Polydimethylsiloxane; Pressure sensor; Wearable computer; Optical fiber; Optoelectronics; Bending; Repeatability; Fiber optic sensor; Plastic optical fiber; Acoustics; Fiber; Composite material; Computer science; Telecommunications; Mechanical engineering; Embedded system; Engineering","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":[0.0002852548,0.0004925249,0.0002831044,0.0002283993,0.0001570743,0.0002524995,0.0005925487,0.0003779846,0.0006192317],"category_scores_gemma":[0.0002060333,0.000320309,0.0002744358,0.0001583335,0.0002153675,0.0005064228,0.0002642265,0.0003330415,0.0002991699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935189,"about_ca_system_score_gemma":0.0003828774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004295648,"about_ca_topic_score_gemma":0.0006779553,"domain_scores_codex":[0.9998096,0.00001330989,0.00001460808,0.00004561912,0.00009666652,0.00002026894],"domain_scores_gemma":[0.9998736,0.00002122334,0.00003725804,0.00001365738,0.00003173283,0.0000224995],"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":[0.00002643576,0.00002506291,0.0002029011,0.00006217024,0.000006248065,0.00009316043,0.00002408468,0.0002094611,0.9926359,0.0001511768,0.00005564848,0.006507853],"study_design_scores_gemma":[0.00001307031,0.000449664,0.001256482,0.000006757801,0.0000163594,0.0002722881,0.00001215222,0.003871706,0.9921308,0.00003084033,0.001926766,0.00001305652],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7895638,0.002124065,0.2026078,0.0004936389,0.000358448,0.0003934863,0.0003851736,0.000758241,0.003315468],"genre_scores_gemma":[0.8273776,0.001474595,0.1655293,0.0002170777,0.00007169538,0.0001737104,0.000227736,0.00004796789,0.004880275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006192317,"threshold_uncertainty_score":0.002071559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00966853754523444,"score_gpt":0.2140263121633719,"score_spread":0.2043577746181374,"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."}}