{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000947732,0.000171222,0.0004997849,0.0001023224,0.00005943938,0.0001420303,0.0005069951,0.0001328536,0.01220108],"category_scores_gemma":[0.00005819694,0.0001578311,0.00009688722,0.0001319796,0.0001113663,0.0001741099,0.00002579433,0.0001639915,0.00001774747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005208682,"about_ca_system_score_gemma":0.003955983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002634814,"about_ca_topic_score_gemma":0.0001804362,"domain_scores_codex":[0.9980996,0.00008848864,0.0006478695,0.0001901538,0.0004254461,0.000548395],"domain_scores_gemma":[0.9984501,0.00003208517,0.0004818082,0.0003053881,0.0002629044,0.000467743],"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":[0.00003911819,0.0000151332,0.001076686,0.0001422745,0.00001593384,0.00004187324,0.00003841886,0.0000609061,0.9983915,0.00001334865,0.0001524891,0.0000122999],"study_design_scores_gemma":[0.0005293874,0.0001057251,0.001209434,0.0001396113,0.00001674366,0.0001020241,0.0000950678,0.00002816507,0.9971023,0.00004135667,0.0004788823,0.0001513604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967018,0.0002377985,0.00003285421,0.00005079884,0.000658532,0.0001187626,0.0000652743,0.000005420612,0.002128749],"genre_scores_gemma":[0.9985223,0.00002067322,0.0009442573,0.00003575614,0.0002279363,0.000001469679,0.000004865628,0.00002611573,0.0002165967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01218333,"threshold_uncertainty_score":0.9887019,"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."}}