{"id":"W2913204347","doi":"10.1139/cjp-2018-0040","title":"Thermoelectric and electronic properties of B-doped graphene nanoribbon","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Graphene research and applications","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Graphene; Seebeck coefficient; Boron; Condensed matter physics; Thermoelectric effect; Materials science; Doping; Fermi level; Fermi energy; Graphene nanoribbons; Density functional theory; Impurity; Bilayer graphene; Nanotechnology; Physics; Electron; Computational chemistry; Thermodynamics; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000148179,0.0003372621,0.0002993203,0.0003739412,0.0002258229,0.0002333587,0.0003502132,0.0005785995,0.0008404648],"category_scores_gemma":[0.0003394252,0.0001852961,0.0003112885,0.0003995944,0.0002002144,0.0003295785,0.0001617759,0.0001942246,0.000187079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049137,"about_ca_system_score_gemma":0.0001021016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009728882,"about_ca_topic_score_gemma":0.001246511,"domain_scores_codex":[0.9998895,0.00001556695,0.000004765153,0.00002237916,0.00004691704,0.00002083051],"domain_scores_gemma":[0.9998809,0.00004144356,0.00002114709,0.00001481192,0.00002888067,0.00001290134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002255332,0.0000355702,0.0007922568,0.0001778374,0.00003370334,0.0002325231,0.00004217565,0.008715249,0.9853842,0.0008034754,0.000100707,0.003456802],"study_design_scores_gemma":[0.00003894593,0.000614752,0.006618087,0.00004138055,0.00005837985,0.0002497959,0.00008272073,0.0748326,0.9148758,0.000864749,0.001661018,0.00006174221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941357,0.001058648,0.002333431,0.00006990215,0.00002691172,0.000005533891,0.0001927879,0.00006343782,0.00211362],"genre_scores_gemma":[0.9968331,0.0002822008,0.002164989,0.00001973467,0.000002494259,0.000005794278,0.0001083693,0.00001803925,0.0005652789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009728882,"threshold_uncertainty_score":0.002811611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211963167087323,"score_gpt":0.2086465110151216,"score_spread":0.1965268793442484,"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."}}