{"id":"W2597576088","doi":"10.1002/jemt.22869","title":"Quality evaluation of ultra‐thin samples: Application to graphene","year":2017,"lang":"en","type":"article","venue":"Microscopy Research and Technique","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology","funders":"National Research Council Canada","keywords":"Graphene; Materials science; Transmission electron microscopy; Monolayer; Nanotechnology; Selected area diffraction; Magnification; Diffraction; Optics; 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":[0.000528257,0.000293233,0.0001418856,0.0007924538,0.0002668809,0.000378405,0.0003744428,0.0004855952,0.0007560175],"category_scores_gemma":[0.000788908,0.0001879769,0.0001242241,0.0003488147,0.0003906018,0.0002380414,0.0003060828,0.0002983118,0.0001045243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004051024,"about_ca_system_score_gemma":0.00008785583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001128368,"about_ca_topic_score_gemma":0.001539994,"domain_scores_codex":[0.999674,0.00005521924,0.00001799105,0.00003801832,0.0001902791,0.00002448727],"domain_scores_gemma":[0.9992582,0.0001211924,0.0001837007,0.00009670152,0.0003038773,0.00003636336],"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":[0.00003307231,0.00001087892,0.0008316525,0.00005881085,0.00001094494,0.00008625941,0.00005478539,0.0003208602,0.9936072,0.0001365569,0.000061718,0.00478738],"study_design_scores_gemma":[0.000005059696,0.00009449075,0.005871716,0.00001375944,0.00002120835,0.0002930367,0.00005497774,0.005022958,0.9873777,0.0001252897,0.001107972,0.00001185553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9283953,0.002654504,0.06560357,0.0001962553,0.00006389127,0.0000896325,0.0002596453,0.0004452679,0.00229197],"genre_scores_gemma":[0.9631657,0.0005763978,0.03528067,0.00004147662,0.000009937866,0.00001889101,0.00009487929,0.00004386439,0.0007680996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001128368,"threshold_uncertainty_score":0.002939224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101928899165804,"score_gpt":0.4794186284875824,"score_spread":0.3774897293217784,"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."}}