{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000356066,0.00010478,0.0001137022,0.0002216822,0.0004114355,0.00004137782,0.000895787,0.00008041318,0.00006310746],"category_scores_gemma":[0.00008194002,0.0001019864,0.00003784082,0.0007343147,0.0005169953,0.0002499819,0.0002921198,0.0006105721,0.00003697088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005272488,"about_ca_system_score_gemma":0.0001145496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000367513,"about_ca_topic_score_gemma":0.005475543,"domain_scores_codex":[0.9989149,0.0001506718,0.0002093641,0.0002325016,0.0001349704,0.0003576194],"domain_scores_gemma":[0.998247,0.0001705348,0.00006043632,0.001294285,0.0001178809,0.0001098639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00001349482,0.0003003908,0.01045543,0.000006009968,0.00000793723,0.000001338157,0.0007641603,0.000002418535,0.03464792,0.9489884,0.001457729,0.003354764],"study_design_scores_gemma":[0.002531652,0.0001629733,0.5124559,0.0001783014,0.0001172976,0.0001564067,0.004976556,0.00713198,0.0490967,0.3478802,0.07386871,0.001443268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8543068,0.03362026,0.02879177,0.02514241,0.0002068717,0.002146535,0.0001482342,0.0006651874,0.0549719],"genre_scores_gemma":[0.9912478,0.001704778,0.006530846,0.0001939345,0.000009474559,0.0002200175,0.00003908588,0.0000128828,0.00004117235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6011082,"threshold_uncertainty_score":0.4158883,"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."}}