{"id":"W3093277599","doi":"10.1016/j.meatsci.2020.108342","title":"Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches","year":2020,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Ministry of Agriculture - Saskatchewan","keywords":"Fatty acid; Chemistry; Food science; Linear discriminant analysis; Subcutaneous fat; Beef cattle; Animal science; Near infrared reflectance spectroscopy; Mathematics; Biology; Near-infrared spectroscopy; Biochemistry","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":[0.0001101052,0.0001222757,0.0002004442,0.00008997918,0.0002185099,0.0001436903,0.0001973566,0.00004361636,0.00009273369],"category_scores_gemma":[0.0002216738,0.0001047382,0.0000267299,0.0006830071,0.0005827363,0.0003350223,0.0001221744,0.00008575387,0.000001008792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004016388,"about_ca_system_score_gemma":0.00005024621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002103237,"about_ca_topic_score_gemma":0.000001356445,"domain_scores_codex":[0.9988796,0.000009096633,0.0001934155,0.0003885563,0.0003318353,0.0001974611],"domain_scores_gemma":[0.9994969,0.00004090632,0.0001106754,0.000175702,0.00004466627,0.0001311614],"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":[0.00001393342,0.00004185141,0.0216129,0.0001175328,0.000009582221,3.371398e-7,0.001555282,0.000009489542,0.9754631,0.0009369549,0.000006726443,0.0002323146],"study_design_scores_gemma":[0.0002155692,0.00003822457,0.01531441,0.00001748285,0.00008830168,0.000002170483,0.0009381515,0.05170032,0.9312047,0.0003516015,0.00001286589,0.0001161519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930798,0.0002762338,0.00340507,0.0002388751,0.00001550878,0.00005338369,0.00000554593,0.00002729241,0.002898221],"genre_scores_gemma":[0.9960011,0.000041995,0.003821235,0.00002821633,0.00002922518,0.000002732865,0.000004529618,0.000007319176,0.00006362916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05169084,"threshold_uncertainty_score":0.42711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.060883754597398,"score_gpt":0.2843959950311893,"score_spread":0.2235122404337913,"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."}}