{"id":"W2913467482","doi":"10.1139/cjc-2018-0430","title":"Assessment of boron nitride nanotube materials using X-ray photoelectron spectroscopy","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Boron and Carbon Nanomaterials Research","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Boron nitride; X-ray photoelectron spectroscopy; Surface modification; Boron; Chemistry; Nanotube; Nitride; Raw material; Chemical engineering; Elemental analysis; Nanotechnology; Characterization (materials science); Analytical Chemistry (journal); Materials science; Carbon nanotube; Inorganic chemistry; Physical chemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0003676526,0.0004415308,0.0002535806,0.0007823425,0.0004494673,0.0005995058,0.0002897967,0.0007154455,0.00174675],"category_scores_gemma":[0.0003572113,0.0001973285,0.0001823762,0.0004316307,0.0002021389,0.0004899057,0.0003194261,0.0003256922,0.0005508334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457654,"about_ca_system_score_gemma":0.0001545961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115797,"about_ca_topic_score_gemma":0.003044813,"domain_scores_codex":[0.9996604,0.00003146489,0.00001419695,0.00004352473,0.000217683,0.00003276454],"domain_scores_gemma":[0.9998286,0.00003845228,0.00002083757,0.00001103784,0.00008924024,0.00001172227],"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":[0.00002756861,0.00001022434,0.000480065,0.00005157672,0.000003798264,0.00005112061,0.00003540032,0.0001470815,0.9965653,0.00009629608,0.00004032282,0.002491354],"study_design_scores_gemma":[0.000002361882,0.0001268813,0.008996656,0.00002013397,0.00001101186,0.0002044988,0.0001017769,0.001724335,0.9841557,0.0002124898,0.004431281,0.00001290074],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265385,0.00427334,0.04885925,0.0001778308,0.00007161003,0.0002734161,0.001863518,0.0004198396,0.01752282],"genre_scores_gemma":[0.926656,0.004584026,0.05517343,0.000120858,0.00001380213,0.0001922903,0.001452764,0.00009023602,0.01171658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00174675,"threshold_uncertainty_score":0.005843461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077926028944893,"score_gpt":0.2724423402975676,"score_spread":0.2616630800081187,"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."}}