{"id":"W1995498083","doi":"10.1116/1.3511506","title":"Monte Carlo modeling of electron backscattering from carbon nanotube forests","year":2010,"lang":"en","type":"article","venue":"Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; University of British Columbia","keywords":"Monte Carlo method; Carbon nanotube; Nanotube; Scattering; Materials science; Electron; Statistical physics; Nanotechnology; Computational physics; Physics; Optics; Nuclear physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005545103,0.0003237481,0.0005477588,0.0004370265,0.0005283525,0.0006226407,0.000792331,0.001105755,0.0009982944],"category_scores_gemma":[0.001368414,0.0004051091,0.0005114087,0.0004723339,0.0006895139,0.0006694937,0.0004068073,0.0004032149,0.0001672746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000857133,"about_ca_system_score_gemma":0.0008126334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01014686,"about_ca_topic_score_gemma":0.005407415,"domain_scores_codex":[0.9997942,0.00006040398,0.000008093099,0.00001798004,0.00007241216,0.00004685449],"domain_scores_gemma":[0.9992622,0.0004652787,0.00005914787,0.00004257086,0.0001095534,0.00006113389],"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":[0.0000173693,0.000008817042,0.0002152168,0.000005751366,0.000005581288,0.00003571866,0.00001193115,0.9944853,0.0006005935,0.004036884,0.00004996038,0.0005268842],"study_design_scores_gemma":[0.000002296664,0.00000255729,0.00003449489,0.000001087048,9.664565e-7,0.000004509319,0.00000187165,0.9992182,0.0001527447,0.0005290578,0.00005019843,0.000001964778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4208393,0.000893168,0.5584782,0.0004254669,0.000100384,0.0001001373,0.0002119925,0.0004314447,0.01851987],"genre_scores_gemma":[0.9721511,0.0002645808,0.02366756,0.00007860864,0.00001903981,0.0001008663,0.00008592613,0.00005846179,0.003573933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01014686,"threshold_uncertainty_score":0.02017558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01074310367694316,"score_gpt":0.2434947537359537,"score_spread":0.2327516500590105,"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."}}