{"id":"W2995101048","doi":"10.1038/s41598-019-55219-2","title":"Unmixing noisy co-registered spectrum images of multicomponent nanostructures","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Spectroscopy; Nanomaterials; Spectral line; SIGNAL (programming language); Materials science; Signal-to-noise ratio (imaging); Noise (video); Multivariate statistics; Biological system; Energy (signal processing); Computer science; Component (thermodynamics); Analytical Chemistry (journal); Pattern recognition (psychology); Physics; Nanotechnology; Chemistry; Artificial intelligence; Optics; Image (mathematics); Mathematics; Statistics; Biology; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0002136122,0.0001231089,0.0002347459,0.00006326988,0.0001114347,0.00009187457,0.0001861119,0.00005713058,0.0008890194],"category_scores_gemma":[0.00003553474,0.0001017232,0.0001626665,0.0002206094,0.0001639574,0.00005624888,0.00005716123,0.000107258,0.00004706932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003427008,"about_ca_system_score_gemma":0.00004238398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003264033,"about_ca_topic_score_gemma":0.000004533226,"domain_scores_codex":[0.9982882,0.000006456235,0.000496604,0.0005838332,0.0003845985,0.0002402813],"domain_scores_gemma":[0.9983814,0.00002958084,0.0004248417,0.001028541,0.00005984339,0.00007579498],"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":[0.000003095996,0.00005027647,0.006321541,0.00006094249,0.00002531046,0.000008944065,0.00004137242,0.00001303856,0.99136,0.00007931019,0.001852283,0.0001838739],"study_design_scores_gemma":[0.00007812259,0.000003334954,0.0005766866,0.00003198688,0.00002335613,0.00003743167,0.0000309221,0.00014169,0.9830843,0.003544765,0.01232898,0.0001184781],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857619,0.0001216671,0.00006623095,0.0001191253,0.0002739338,0.00007824435,0.000007174712,0.00004355839,0.01352815],"genre_scores_gemma":[0.9918619,0.000001878661,0.0005903985,0.000008719342,0.00004038419,0.000007617083,0.0001317456,0.00001114928,0.007346157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0104767,"threshold_uncertainty_score":0.9734139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008387631123871923,"score_gpt":0.2490053667824515,"score_spread":0.2406177356585796,"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."}}