{"id":"W2976352374","doi":"10.1016/j.optlastec.2019.105836","title":"Thermal gradients sensing using LPGs with a spatially varying effective refractive index difference","year":2019,"lang":"en","type":"article","venue":"Optics & Laser Technology","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Refractive index; Materials science; Optics; Index (typography); Thermal; Optoelectronics; Physics; Computer science; Meteorology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005540779,0.0003363683,0.0003835911,0.0003388607,0.00007812521,0.00002567569,0.0002067938,0.0003212033,0.00001025832],"category_scores_gemma":[0.00004289766,0.0003160851,0.00003867225,0.0005400954,0.0001475971,0.0001580192,0.0001019729,0.0006710845,0.00004919916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002419187,"about_ca_system_score_gemma":0.00002366522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001953787,"about_ca_topic_score_gemma":0.00001579518,"domain_scores_codex":[0.9986079,0.00002282457,0.000215472,0.000405254,0.0001895994,0.0005588905],"domain_scores_gemma":[0.9990636,0.0001403413,0.00009229358,0.000533891,0.000106071,0.00006386964],"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":[0.0001212901,0.00004638918,0.012811,0.00006827903,0.0002714984,0.0001667293,0.0004857487,0.6637895,0.2971002,0.000458112,7.076974e-7,0.02468065],"study_design_scores_gemma":[0.00149887,0.0003066505,0.003126388,0.0002579197,0.00008479639,0.0001442579,0.000251385,0.8175042,0.1752626,0.0007869243,0.00004689191,0.0007291059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944534,0.00003328842,0.05240511,0.00002826853,0.0002539893,0.0005295214,0.000004689795,0.0006811246,0.001530028],"genre_scores_gemma":[0.9447956,0.000006983744,0.05498572,0.00002180249,0.00003164616,0.0000100151,0.000003351697,0.0001046513,0.00004022273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1537148,"threshold_uncertainty_score":0.9999291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005044120324416241,"score_gpt":0.2082698069328899,"score_spread":0.2032256866084737,"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."}}