{"id":"W2084537152","doi":"10.1117/12.631347","title":"Determination of pork quality attributes using hyperspectral imaging technique","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Repeatability; Grading (engineering); Computer science; Correlation coefficient; Artificial neural network; Quality (philosophy); Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Machine learning","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":[0.0003172344,0.0003509095,0.0002234081,0.0003240491,0.00009241179,0.0002480791,0.0001694222,0.0002471862,0.0003883907],"category_scores_gemma":[0.0006115807,0.0001475318,0.000174871,0.0002243013,0.000158843,0.0003879206,0.000155683,0.0002848773,0.0001878642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835608,"about_ca_system_score_gemma":0.0001072558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009954186,"about_ca_topic_score_gemma":0.001648892,"domain_scores_codex":[0.9998073,0.00003756911,0.000006664544,0.0000469703,0.00008968394,0.00001187506],"domain_scores_gemma":[0.9998201,0.00005418887,0.00003795605,0.00001411979,0.00006690883,0.000006919222],"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":[0.0001479094,0.00007767527,0.00819711,0.0001130626,0.00003623401,0.00004692201,0.00007845175,0.006812426,0.8882564,0.0001817664,0.0002170928,0.09583496],"study_design_scores_gemma":[0.00001144022,0.0002287089,0.04634374,0.00001088742,0.00004314033,0.0003043268,0.00006331586,0.2905591,0.660899,0.0003160074,0.001182968,0.00003734018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7216823,0.0004482045,0.2753631,0.0001154048,0.00001637283,0.00004385973,0.0001990539,0.0004731352,0.001658608],"genre_scores_gemma":[0.8720872,0.0004552863,0.1258883,0.00005611448,0.00001161319,0.0000323103,0.0001852653,0.00002190507,0.00126192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009954186,"threshold_uncertainty_score":0.001979232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941649154400103,"score_gpt":0.2829794973865742,"score_spread":0.2635630058425732,"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."}}