{"id":"W4225275842","doi":"10.1002/jrs.6371","title":"Hyperspectral Raman imaging and multivariate statistical analysis for the reconstruction of obliterated serial numbers in polymers","year":2022,"lang":"en","type":"article","venue":"Journal of Raman Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Centre for Comparative Criminology; Université du Québec à Trois-Rivières; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Computer science; Contrast (vision); Pixel; Raman spectroscopy; Biological system; Materials science; Optics; Physics; Biology","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":[0.0008254758,0.0004988064,0.0002349552,0.001189482,0.0001664062,0.0004720856,0.0002923938,0.0002808689,0.001096474],"category_scores_gemma":[0.00113948,0.0001835788,0.0004498262,0.0009094792,0.00039217,0.0005212906,0.0003321179,0.000523573,0.000314003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002137021,"about_ca_system_score_gemma":0.0003980886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006783757,"about_ca_topic_score_gemma":0.001023267,"domain_scores_codex":[0.9996191,0.0001068001,0.00001635251,0.00006467277,0.0001707409,0.00002231149],"domain_scores_gemma":[0.9993117,0.0002778463,0.000150914,0.00007622514,0.0001587655,0.00002451463],"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":[0.0003044445,0.0002373842,0.005258372,0.0003332634,0.00009042427,0.0002519484,0.0002127396,0.0579993,0.6192874,0.008594751,0.001128229,0.3063017],"study_design_scores_gemma":[0.000007575547,0.0001339467,0.008078847,0.00001817302,0.00002969541,0.0002252612,0.0001019688,0.8050141,0.1819607,0.002473327,0.001898141,0.00005835232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2389963,0.0005043565,0.757404,0.0002219613,0.00004610411,0.00006658401,0.0002536585,0.000882542,0.001624471],"genre_scores_gemma":[0.616452,0.0005496813,0.3813496,0.0000356973,0.00002950044,0.00006453881,0.0001894179,0.0001002431,0.001229341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001189482,"threshold_uncertainty_score":0.004365623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008200858781941624,"score_gpt":0.3166803734490979,"score_spread":0.3084795146671563,"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."}}