{"id":"W3187695604","doi":"10.1017/s1431927621005997","title":"EELSpecNet: Deep Convolutional Neural Network Solution for Electron Energy Loss Spectroscopy Deconvolution","year":2021,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Deconvolution; Convolutional neural network; Computer science; Spectroscopy; Energy (signal processing); Content (measure theory); Electron energy loss spectroscopy; Artificial intelligence; Biological system; Algorithm; Electron; Physics; Mathematics; Statistics; Nuclear physics; Astronomy; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004998262,0.0004420042,0.0006523147,0.0001611653,0.0008008409,0.0002763381,0.0002823403,0.000250549,0.0004917401],"category_scores_gemma":[0.00004055363,0.0004599096,0.0003395317,0.0005755837,0.0002487994,0.0003144155,0.0001228527,0.0002215571,0.0000219684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000349127,"about_ca_system_score_gemma":0.0002257176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002041966,"about_ca_topic_score_gemma":0.001321626,"domain_scores_codex":[0.9966566,0.000196529,0.0006105754,0.001040368,0.0002480597,0.001247862],"domain_scores_gemma":[0.9986641,0.0001148805,0.0002694591,0.000460544,0.000300514,0.0001904805],"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":[0.0002275759,0.0001125299,0.001009164,0.0000294509,0.0001031495,0.000008856951,0.00004423328,0.00007625005,0.9850675,0.007586712,0.00529114,0.0004434217],"study_design_scores_gemma":[0.0006739033,0.0002693652,0.0003603678,0.00002543926,0.000332605,0.0001174526,0.00002169413,0.004329795,0.9733728,0.006192525,0.01380786,0.0004962531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5148529,0.01911168,0.4635838,0.001033724,0.0004377314,0.000293881,0.00008897098,0.0003107338,0.0002865195],"genre_scores_gemma":[0.9039286,0.001996137,0.08917172,0.001415936,0.0008287576,0.0001606032,0.0005780577,0.00008075614,0.00183942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3890757,"threshold_uncertainty_score":0.9997852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007367875234645449,"score_gpt":0.2631715890837902,"score_spread":0.2558037138491447,"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."}}