{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006316195,0.0009755479,0.0005496311,0.001709951,0.0003678996,0.0006601234,0.0004809799,0.0006931726,0.001323495],"category_scores_gemma":[0.001085071,0.0003373964,0.0007001387,0.001451779,0.0004688297,0.001245433,0.0007395796,0.0007824334,0.0004640323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002928302,"about_ca_system_score_gemma":0.0004961459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009762238,"about_ca_topic_score_gemma":0.002586755,"domain_scores_codex":[0.9996118,0.00003475191,0.00002495954,0.000144673,0.0001453212,0.00003855081],"domain_scores_gemma":[0.9996747,0.00007697161,0.00004974024,0.0000759788,0.0001058202,0.00001690407],"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":[0.0004290257,0.0002066974,0.002718838,0.0004778372,0.0001498783,0.000304714,0.0003667191,0.01898672,0.8044813,0.002137743,0.001161452,0.1685791],"study_design_scores_gemma":[0.00002681603,0.0002130921,0.01696462,0.00004235935,0.0001747644,0.0009718357,0.0004077597,0.4324384,0.5329171,0.005816396,0.009913298,0.0001134654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3507266,0.000679128,0.6425905,0.0002172038,0.0001422449,0.0001254632,0.0007725442,0.00187254,0.002873786],"genre_scores_gemma":[0.4422222,0.0006315252,0.5527678,0.00008266289,0.0000495116,0.0001378341,0.001383595,0.0004138517,0.002311151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001709951,"threshold_uncertainty_score":0.004427493,"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."}}