{"id":"W2027488899","doi":"10.1038/ncomms1376","title":"Imaging local electronic corrugations and doped regions in graphene","year":2011,"lang":"en","type":"article","venue":"Nature Communications","topic":"Graphene research and applications","field":"Materials Science","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Basic Energy Sciences; Division of Materials Research; Office of Science; National Institute of Standards and Technology; New York State Energy Research and Development Authority; U.S. Department of Energy; Canadian Light Source; National Science Foundation","keywords":"Graphene; Density functional theory; Materials science; Doping; Local density of states; Electronic structure; Microscopy; High-resolution transmission electron microscopy; Spectroscopy; Molecular physics; Nanotechnology; Optics; Condensed matter physics; Optoelectronics; Transmission electron microscopy; Chemistry; Physics; Computational chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00008474733,0.0001928381,0.0001019541,0.0004595775,0.0002954263,0.0004484836,0.000330058,0.0004389868,0.001747735],"category_scores_gemma":[0.000223025,0.0002251566,0.00009698565,0.0003006347,0.0005184779,0.0004821167,0.000360566,0.0004310548,0.000163358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319039,"about_ca_system_score_gemma":0.0001248899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005462325,"about_ca_topic_score_gemma":0.001415369,"domain_scores_codex":[0.9999501,0.000005775486,0.000001555425,0.00001425921,0.00001510076,0.0000132329],"domain_scores_gemma":[0.9998722,0.00004934294,0.00003125131,0.00001766494,0.00001163529,0.00001791195],"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":[0.0002170948,0.00003000221,0.001030506,0.00006439741,0.00000970427,0.0002618501,0.00009774922,0.0006652622,0.9920812,0.001385601,0.0001596986,0.003996952],"study_design_scores_gemma":[0.00003109839,0.0001403557,0.007717574,0.00001756823,0.00002728557,0.0006974898,0.0002113681,0.008157736,0.9808779,0.0007626959,0.001338805,0.00002014254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920595,0.0003805529,0.004164791,0.0001003539,0.00001642008,0.000008421235,0.0001031192,0.00007834764,0.003088542],"genre_scores_gemma":[0.99364,0.0001862957,0.004630029,0.00004015311,0.000009311775,0.000007195129,0.00004303439,0.00001584891,0.001428208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001747735,"threshold_uncertainty_score":0.005846739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663262743300331,"score_gpt":0.3031650104707316,"score_spread":0.2765323830377283,"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."}}