{"id":"W2539022462","doi":"10.1017/s1431927613011689","title":"Identifying the Optical Response of Graphene Using Electron Energy-Loss Spectroscopy","year":2013,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Graphene; Spectroscopy; Microanalysis; Electron energy loss spectroscopy; Materials science; Electron microscope; Electron; Energy (signal processing); Electron probe microanalysis; Nanotechnology; Analytical Chemistry (journal); Engineering physics; Optics; Scanning electron microscope; Physics; Chemistry; Transmission electron microscopy; Nuclear physics; Environmental chemistry; Astronomy; Composite material; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001250474,0.0002451868,0.00008480263,0.000644753,0.0001708462,0.0003261412,0.0003327213,0.0003421926,0.004302619],"category_scores_gemma":[0.0003014633,0.0001090832,0.0001098554,0.0002886263,0.0002127011,0.0003336698,0.000281172,0.0003102888,0.0004418877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422481,"about_ca_system_score_gemma":0.00007245388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000532083,"about_ca_topic_score_gemma":0.001742104,"domain_scores_codex":[0.9999121,0.00000954295,0.000002500563,0.00001708737,0.00004176541,0.00001697478],"domain_scores_gemma":[0.9999063,0.00004173662,0.00001481661,0.00001026269,0.00001873797,0.000008144128],"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":[0.00007889775,0.00002395032,0.0009219082,0.00005330741,0.000008731339,0.0000783317,0.00003215361,0.0003497054,0.990541,0.000477883,0.0003989486,0.007035189],"study_design_scores_gemma":[0.000005236627,0.00006876308,0.00546159,0.000007430308,0.0000135254,0.0001782657,0.00007585213,0.005814528,0.9862999,0.0003534048,0.001710549,0.00001096122],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736357,0.0009557835,0.0169962,0.0002760759,0.00009304823,0.00002879357,0.0005296213,0.0002008526,0.00728385],"genre_scores_gemma":[0.9873253,0.000457815,0.008095058,0.0001057846,0.00001252383,0.0000149335,0.0002119686,0.00002207731,0.003754625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004302619,"threshold_uncertainty_score":0.01439369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155071250044469,"score_gpt":0.2890825739571278,"score_spread":0.2775318614566831,"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."}}