{"id":"W3091741220","doi":"10.1109/lsens.2020.3023702","title":"Boron Nitride Nanotubes for Optical Fiber Chemical Sensing Applications","year":2020,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Diamond and Carbon-based Materials Research","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Materials science; Boron nitride; Acetone; Chemical engineering; Tetrahydrofuran; Fiber; Nanomaterials; Selectivity; Optical fiber; Fiber optic sensor; Surface modification; Nanotechnology; Organic chemistry; Composite material; Solvent; Chemistry; Optics","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":[0.0001730212,0.0004193488,0.000195342,0.0003325475,0.0002362489,0.0002344526,0.000233409,0.0006029455,0.003891239],"category_scores_gemma":[0.0001323088,0.0001397402,0.0001558741,0.0002849748,0.0001207547,0.0003343484,0.0002022447,0.0004158813,0.001637702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004182393,"about_ca_system_score_gemma":0.0001951699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004407795,"about_ca_topic_score_gemma":0.001089892,"domain_scores_codex":[0.9998815,0.00001458752,0.000006300746,0.00002315806,0.00005570216,0.00001872572],"domain_scores_gemma":[0.9999448,0.000009318317,0.00001012824,0.000006413804,0.0000195503,0.000009835048],"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":[0.00005244735,0.0000266021,0.0001537514,0.0002691133,0.000006800665,0.00009276425,0.00002262778,0.0003246185,0.9489223,0.004525613,0.002710453,0.04289291],"study_design_scores_gemma":[0.00001833691,0.0002256656,0.001071476,0.00007808387,0.00001824327,0.0004655568,0.00002777475,0.004032633,0.8157113,0.002046339,0.1762789,0.00002553367],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3110614,0.2053799,0.2804073,0.005339129,0.003059067,0.0005560286,0.002398295,0.002384151,0.1894147],"genre_scores_gemma":[0.7555527,0.05014884,0.1507105,0.0008094899,0.0003678238,0.0002662108,0.001117561,0.0001321118,0.04089469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003891239,"threshold_uncertainty_score":0.01301748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141036753232818,"score_gpt":0.2613528724538246,"score_spread":0.2399425049214964,"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."}}