{"id":"W3194263458","doi":"10.1007/s12161-021-02113-1","title":"Improving Intramuscular Fat Assessment in Pork by Synergy Between Spectral and Spatial Features in Hyperspectral Image","year":2021,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; McGill University","funders":"Swine Innovation Porc","keywords":"Hyperspectral imaging; Intramuscular fat; Image fusion; Pattern recognition (psychology); Sensor fusion; Artificial intelligence; Computer science; Fusion; Filter (signal processing); Support vector machine; Fuse (electrical); Principal component analysis; Computer vision; Image (mathematics); Food science; Chemistry; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004912514,0.0003191151,0.0007724773,0.0002370872,0.00005732187,0.0001061807,0.0002196284,0.0002934368,0.0006034087],"category_scores_gemma":[0.0007420662,0.0003102883,0.000187871,0.00101408,0.0001382329,0.0001150288,0.0001534633,0.0009840713,0.000001255774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003085913,"about_ca_system_score_gemma":0.0001088835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007988016,"about_ca_topic_score_gemma":0.0002355394,"domain_scores_codex":[0.9975761,0.0002141325,0.0005049098,0.0007415327,0.0003083958,0.0006549026],"domain_scores_gemma":[0.9987385,0.0005454976,0.0000920637,0.000349479,0.0000461539,0.0002283455],"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":[0.00006265183,0.0006205481,0.07152043,0.0002931214,0.0006755504,0.0004069054,0.0001656324,0.00002176188,0.8818483,0.005165013,0.0001545861,0.03906547],"study_design_scores_gemma":[0.00143606,0.0001827225,0.03140289,0.00005197458,0.0004732766,0.00003138565,0.001529946,0.005063901,0.9559354,0.00303344,0.0001822562,0.000676768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9019443,0.002973488,0.07870707,0.001264759,0.00005932834,0.00009091281,0.00006233757,0.00008712748,0.01481063],"genre_scores_gemma":[0.8889736,0.0000633299,0.1102853,0.00005740813,0.0002552187,0.000009920921,0.00005242938,0.00003021988,0.0002725353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07408703,"threshold_uncertainty_score":0.9999349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01663848377132486,"score_gpt":0.3561505676517951,"score_spread":0.3395120838804702,"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."}}