{"id":"W4212948524","doi":"10.1016/j.meatsci.2022.108774","title":"Application of Vis-NIR and SWIR spectroscopy for the segregation of bison muscles based on their color stability","year":2022,"lang":"en","type":"article","venue":"Meat Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Principal component analysis; Near-infrared spectroscopy; Linear discriminant analysis; Calibration; Spectroscopy; Analytical Chemistry (journal); Stability (learning theory); Materials science; Environmental science; Chemistry; Mathematics; Optics; Chromatography; Statistics; Physics; Computer science","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":[0.0006593563,0.0006893543,0.0002525515,0.001102337,0.0003616059,0.0004971938,0.0002554449,0.0005274864,0.000836735],"category_scores_gemma":[0.0006654657,0.0002829239,0.0002939437,0.0005505027,0.000373425,0.0003393487,0.0003396887,0.0005981862,0.000328141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001718051,"about_ca_system_score_gemma":0.0002574805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001191536,"about_ca_topic_score_gemma":0.003122443,"domain_scores_codex":[0.9996415,0.0000794749,0.00001342341,0.0001114714,0.0001179114,0.00003614751],"domain_scores_gemma":[0.9996521,0.00009440311,0.00005139411,0.00001620479,0.000144695,0.00004119146],"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":[0.0001963817,0.00002541819,0.001087866,0.00004964472,0.00001511596,0.00001305597,0.00003290118,0.00004763385,0.9915317,0.00006197678,0.00003071712,0.006907577],"study_design_scores_gemma":[0.000007137036,0.0002873458,0.01574001,0.00001386529,0.00007471498,0.0002336876,0.0001152491,0.003793299,0.9783745,0.0001182544,0.001214421,0.00002755857],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249744,0.006355516,0.06352405,0.0001962923,0.0001578529,0.00008225887,0.000396188,0.0002010286,0.004112293],"genre_scores_gemma":[0.9170419,0.002906496,0.07539381,0.0001887165,0.0000457963,0.00007882027,0.000303643,0.00005063748,0.00399011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001191536,"threshold_uncertainty_score":0.003487051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208892339977628,"score_gpt":0.27930017266845,"score_spread":0.2584109386706872,"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."}}