{"id":"W3092119540","doi":"10.1364/noma.2020.noth1c.3","title":"High refractive index polydimethylsiloxane waveguides for biomedical photonics sensors","year":2020,"lang":"en","type":"article","venue":"OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital du Sacré-Cœur de Montréal; École de Technologie Supérieure","funders":"","keywords":"Polydimethylsiloxane; Refractive index; Materials science; High-refractive-index polymer; Photonics; Benzophenone; Contrast (vision); Optoelectronics; Refractometry; Refractive index contrast; High contrast; Optics; Nanotechnology; Polymer chemistry; Fabrication","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0006773359,0.001847691,0.002486054,0.0002760884,0.0005806829,0.0003559754,0.001743661,0.001300657,0.000815897],"category_scores_gemma":[0.0006347793,0.001900973,0.0008318353,0.001682666,0.0007158631,0.0009877987,0.0005949907,0.002491781,0.0001877513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006508308,"about_ca_system_score_gemma":0.000307229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001027756,"about_ca_topic_score_gemma":0.00009435793,"domain_scores_codex":[0.9914467,0.0002298303,0.002130512,0.002196023,0.001177206,0.002819716],"domain_scores_gemma":[0.9934428,0.001950887,0.0006443608,0.001551782,0.0004809504,0.001929175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005237221,0.0009732567,0.0004461821,0.001819503,0.003547419,0.001179334,0.001746345,0.9033546,0.02522156,0.006779964,0.02943975,0.02025483],"study_design_scores_gemma":[0.005065408,0.0005479334,0.00008833496,0.0002619867,0.0003251604,0.00006344864,0.0003969485,0.8028985,0.01766687,0.0009686734,0.1696732,0.002043567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7806657,0.03604373,0.1102563,0.006039151,0.02586255,0.01300735,0.004329827,0.007881205,0.01591417],"genre_scores_gemma":[0.9386048,0.006263501,0.04811528,0.002755168,0.0009613114,0.000953831,0.0009689488,0.0007321395,0.0006450244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1579391,"threshold_uncertainty_score":0.9999959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009679534101542302,"score_gpt":0.2364710448637483,"score_spread":0.226791510762206,"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."}}