{"id":"W2059785057","doi":"10.1017/s1431927613013676","title":"A “Thickness Series”: Weak Signal Extraction of ELNES in EELS Spectra From Surfaces","year":2013,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electronic and Structural Properties of Oxides","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Series (stratigraphy); Spectral line; Materials science; Enhanced Data Rates for GSM Evolution; Monolayer; Energy (signal processing); Surface (topology); Electron energy loss spectroscopy; Simple (philosophy); Electron; Computational physics; Molecular physics; Analytical Chemistry (journal); Chemistry; Nanotechnology; Geometry; Physics; Computer science; Mathematics; Nuclear physics; Transmission electron microscopy; Geology","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":[0.0003147196,0.0008712909,0.0002528312,0.001110979,0.0002624191,0.0003937448,0.0006886002,0.0003483689,0.001403404],"category_scores_gemma":[0.0004816052,0.0003874977,0.0002856818,0.0004137809,0.0004575941,0.0007293267,0.0005061327,0.0008841286,0.0004424544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295056,"about_ca_system_score_gemma":0.0001238893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002489708,"about_ca_topic_score_gemma":0.0005404526,"domain_scores_codex":[0.999795,0.00002173082,0.00001103139,0.00005077636,0.0001035157,0.00001784782],"domain_scores_gemma":[0.9996959,0.0000791748,0.00007037945,0.00006257051,0.00007272283,0.0000192596],"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":[0.00004311108,0.0000141515,0.0005638707,0.00004330649,0.00001280425,0.00005997232,0.00003930366,0.0002167486,0.9911368,0.000144626,0.00007852838,0.007646802],"study_design_scores_gemma":[0.000004458062,0.00005550262,0.002452219,0.000005459657,0.0000171655,0.0001639834,0.00002275756,0.003533301,0.9923387,0.0001737844,0.001220874,0.00001185923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.802991,0.001449464,0.1859642,0.0002328438,0.0001475881,0.0001292261,0.0008295735,0.001627568,0.006628615],"genre_scores_gemma":[0.8152198,0.001462932,0.1762657,0.0001814604,0.00007472127,0.000128559,0.000937833,0.0003286309,0.005400427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001403404,"threshold_uncertainty_score":0.004694819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008075141174098131,"score_gpt":0.2366129705506108,"score_spread":0.2285378293765127,"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."}}