{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001949477,0.0001744919,0.0003934797,0.0001024709,0.0001070666,0.0001186456,0.0001828839,0.0000972194,0.002230828],"category_scores_gemma":[0.00001567931,0.0001368443,0.00007484848,0.000200907,0.000213611,0.0005645542,0.00005048953,0.000130937,0.00004661037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004123244,"about_ca_system_score_gemma":0.00004127149,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104835,"about_ca_topic_score_gemma":0.00107651,"domain_scores_codex":[0.9987537,0.00008987451,0.0003910933,0.0003434973,0.0001221305,0.0002997627],"domain_scores_gemma":[0.9994791,0.00006038286,0.0001548317,0.0001834228,0.0000708102,0.00005147729],"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":[0.00006493962,0.00002280942,0.003947381,0.00002354416,0.00003352968,0.000001365271,0.0003635739,0.00004254581,0.9944419,0.0000303321,0.0001027254,0.000925368],"study_design_scores_gemma":[0.0002145272,0.00005485326,0.01427331,0.00003536113,0.00006181211,0.000008817888,0.0007840603,0.0001121791,0.9829413,0.001151186,0.0001910908,0.0001714691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995627,0.003485472,0.000236257,0.0002560645,0.00006124034,0.0001236369,0.00002401626,0.00002096003,0.0001653332],"genre_scores_gemma":[0.9950825,0.0003043327,0.003888981,0.00003417784,0.00002709252,0.000006386812,0.00001208448,0.00001017494,0.0006342517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01150055,"threshold_uncertainty_score":0.9986812,"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."}}