{"id":"W2005251888","doi":"10.1366/13-07242","title":"Near-Infrared Spectral Image Analysis of Pork Marbling Based on Gabor Filter and Wide Line Detector Techniques","year":2014,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"","keywords":"Calibration; Marbled meat; Detector; Hyperspectral imaging; Near-infrared spectroscopy; Filter (signal processing); Artificial intelligence; Line (geometry); Pattern recognition (psychology); Computer science; Linear regression; Wavelength; Mathematics; Computer vision; Optics; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002736867,0.0004422151,0.0009546649,0.0006214872,0.0001619862,0.0001231911,0.0003565612,0.0002295724,0.003100773],"category_scores_gemma":[0.0001622894,0.0004182628,0.0002937028,0.001765534,0.0002546369,0.00007101137,0.00006637557,0.0004151317,0.00001811862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219634,"about_ca_system_score_gemma":0.00005346575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005733458,"about_ca_topic_score_gemma":0.00001411298,"domain_scores_codex":[0.997671,0.00002339597,0.0005696487,0.0007299481,0.0004354585,0.0005705572],"domain_scores_gemma":[0.9980844,0.0004694478,0.0003344394,0.0008579893,0.00006893613,0.0001847668],"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":[0.0003507024,0.0001834335,0.006863468,0.00009778806,0.0005683148,0.000004645057,0.00004876675,0.00009851928,0.9905399,0.000589453,0.0003866333,0.0002683963],"study_design_scores_gemma":[0.0006008763,0.0001641977,0.002197487,0.00002294809,0.001527087,8.228692e-7,0.00005055241,0.01130239,0.9824629,0.0007230137,0.0005387478,0.0004089049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8559204,0.0001182991,0.04689347,0.0002125412,0.00002778342,0.0002128733,0.00009422601,0.0004477947,0.09607256],"genre_scores_gemma":[0.9392906,0.0000331826,0.05955989,0.0004449279,0.0001480432,0.00004632116,0.00009473573,0.00006082096,0.0003215145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09575105,"threshold_uncertainty_score":0.9998269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007782626778182132,"score_gpt":0.2528693925187818,"score_spread":0.2450867657405996,"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."}}