{"id":"W1766268931","doi":"10.1063/1.4931676","title":"Electron percolation in realistic models of carbon nanotube networks","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Carbon Nanotubes in Composites","field":"Materials Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Regroupement Québécois sur les Matériaux de Pointe","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Carbon nanotube; Waviness; Materials science; Percolation threshold; Percolation (cognitive psychology); Nanotube; Monte Carlo method; Nanotechnology; Volume fraction; Carbon nanotube actuators; Aspect ratio (aeronautics); Conductivity; Electrical resistivity and conductivity; Composite material; Optical properties of carbon nanotubes; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006208016,0.0001133145,0.0003370477,0.00007370017,0.0000138264,0.00002389589,0.0002305096,0.00006884612,0.000002281835],"category_scores_gemma":[0.00001777273,0.000104092,0.00005649767,0.000265244,0.00004882248,0.0001516122,0.00004719556,0.0001955055,8.794239e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001876907,"about_ca_system_score_gemma":0.0001574105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005400275,"about_ca_topic_score_gemma":0.000009661475,"domain_scores_codex":[0.9987016,0.00004197715,0.0005229766,0.0001150443,0.0004263182,0.0001921031],"domain_scores_gemma":[0.9989825,0.0001031101,0.0004546889,0.0001801274,0.0002070557,0.00007246473],"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":[0.000222645,0.00006249214,0.00007259332,0.000007923508,0.000004649289,0.000002312546,0.0004103002,0.6052726,0.3891987,0.004535022,0.00006522122,0.0001455489],"study_design_scores_gemma":[0.001243446,0.000266769,0.0001667834,0.00007921494,0.00005745078,0.00001630193,0.0001013076,0.5039132,0.406435,0.08748894,0.00002953758,0.0002020262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890786,0.0001338547,0.004719257,0.0000120973,0.0002793237,0.0001082923,0.000001694891,0.000008680116,0.00565814],"genre_scores_gemma":[0.99809,0.00001546542,0.001491228,0.00001918475,0.0003581164,0.000002748784,0.000001582353,0.00001605474,0.000005569954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1013593,"threshold_uncertainty_score":0.4244751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104451457742559,"score_gpt":0.2510012450860656,"score_spread":0.22995673050864,"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."}}