{"id":"W2606622195","doi":"10.1016/j.foodcont.2017.04.036","title":"Discrimination of gluten-free oats from contaminants using near infrared hyperspectral imaging technique","year":2017,"lang":"en","type":"article","venue":"Food Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Principal component analysis; Linear discriminant analysis; Gluten; Partial least squares regression; Near-infrared spectroscopy; Chemometrics; Avena; Food science; Mathematics; Agronomy; Chemistry; Computer science; Artificial intelligence; Biology; Statistics; Chromatography","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":[0.00009847278,0.0001933336,0.0004109519,0.00008192761,0.0003215677,0.0001406778,0.0007083881,0.0001148212,0.000296208],"category_scores_gemma":[0.0005079916,0.0001884877,0.0001531522,0.00008004331,0.0001804352,0.0002965316,0.0001049068,0.0001777638,0.000003651214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001173021,"about_ca_system_score_gemma":0.00007748798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008637369,"about_ca_topic_score_gemma":0.000047208,"domain_scores_codex":[0.9988556,0.0000157446,0.0003006978,0.0002972717,0.0002417423,0.0002889613],"domain_scores_gemma":[0.9982944,0.00008774694,0.0004374274,0.0009911838,0.0001182843,0.00007094428],"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":[0.0001219561,0.000107682,0.04358501,0.00004543782,0.0002567928,0.00001358724,0.0001831819,0.000004892151,0.9539677,0.0002202207,0.0000999015,0.001393622],"study_design_scores_gemma":[0.003089039,0.0000631634,0.01361606,0.00009180605,0.000556429,0.000007972862,0.0003908092,0.00460702,0.9736039,0.00359316,0.00009296736,0.0002876667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741536,0.001186343,0.01181446,0.0002413402,0.00009488473,0.0001735916,0.0003229186,0.00009042892,0.01192236],"genre_scores_gemma":[0.9959396,0.00001157633,0.003644777,0.00003395635,0.0001540549,0.00002284129,0.00001725239,0.00002592899,0.0001500304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02996895,"threshold_uncertainty_score":0.7686306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074509264849152,"score_gpt":0.2785011450259506,"score_spread":0.2577560523774591,"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."}}