{"id":"W1971966977","doi":"10.1016/j.chemolab.2006.08.006","title":"Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites","year":2006,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Principal component analysis; Self-organizing map; Multivariate statistics; Pattern recognition (psychology); Sampling (signal processing); Similarity (geometry); Linear discriminant analysis; Mathematics; Artificial intelligence; Artificial neural network; Computer science; Statistics; Biological system; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003431503,0.0007133737,0.0006250946,0.001378721,0.0004422975,0.0007396233,0.0006709002,0.0002907726,0.001462169],"category_scores_gemma":[0.008770507,0.000262531,0.0007905437,0.001372217,0.0002804135,0.0004573046,0.0006182957,0.0008989961,0.0003070333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003941805,"about_ca_system_score_gemma":0.0009084364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002123416,"about_ca_topic_score_gemma":0.003764977,"domain_scores_codex":[0.9977943,0.001291798,0.0001773005,0.0002620062,0.0004147276,0.00005979458],"domain_scores_gemma":[0.9939135,0.004245989,0.0004833723,0.000447799,0.0007910716,0.0001181446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009867866,0.0003710657,0.02843209,0.0003708287,0.0005526156,0.0000747798,0.0003644374,0.01436887,0.05864728,0.004521191,0.002988986,0.8883211],"study_design_scores_gemma":[0.0002163195,0.001155882,0.2389693,0.000131272,0.0006921112,0.0006069461,0.0004719732,0.6776834,0.04591821,0.02020989,0.01358978,0.000354997],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1090308,0.0004582632,0.8871748,0.0002005818,0.00006036358,0.0001791677,0.001007144,0.001138583,0.0007502728],"genre_scores_gemma":[0.3846069,0.0002795042,0.6111956,0.00008325665,0.0000880207,0.0007792767,0.001420726,0.0004318366,0.001115021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003431503,"threshold_uncertainty_score":0.01814771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06385424401625817,"score_gpt":0.3444248297483865,"score_spread":0.2805705857321283,"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."}}