{"id":"W4376276625","doi":"10.1016/j.meatsci.2023.109222","title":"Evaluating the effect of temperature and multiple bends on an automated pork belly firmness conveyor belt classification system","year":2023,"lang":"en","type":"article","venue":"Meat Science","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Alberta Crop Industry Development Fund; Agriculture and Agri-Food Canada","funders":"Simon Fraser University; Guelph Research and Development Centre, Agriculture and Agri-Food Canada; Swine Innovation Porc","keywords":"Conveyor belt; Bending; Belt conveyor; Mathematics; Stepwise regression; Food science; Materials science; Chemistry; Statistics; Composite material; Mechanical engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.003748341,0.0001100769,0.000152866,0.00001947196,0.0007871655,0.0001020171,0.0003760569,0.00005138791,0.000008166709],"category_scores_gemma":[0.0005268988,0.00003269973,0.00002882899,0.001215097,0.0003192787,0.0001630472,0.00007092832,0.00009262623,0.00001286594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000177276,"about_ca_system_score_gemma":0.00001242335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001084979,"about_ca_topic_score_gemma":0.00003682526,"domain_scores_codex":[0.998326,0.0003281772,0.0001878232,0.0004186873,0.0005095941,0.0002296952],"domain_scores_gemma":[0.9991168,0.0004806834,0.0001005341,0.0001238621,0.0001028611,0.00007521627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003669775,0.000009547856,0.01363168,0.00002909442,0.000001500812,4.232002e-7,0.0001505368,0.00002543031,0.9779839,0.0003553604,0.00003834648,0.00773747],"study_design_scores_gemma":[0.0001353545,0.00140463,0.7556617,0.00006554627,0.000009075929,0.000003621559,0.0007998788,0.05503138,0.1867389,0.0000148744,0.00002537121,0.0001096548],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984708,0.00002538977,8.30974e-8,0.0006055536,0.0001442328,0.0003064563,0.00001763968,0.0002406251,0.0001891531],"genre_scores_gemma":[0.9997529,0.000002143533,0.00001238704,0.00003037464,0.00009010581,0.00001065969,0.0000250524,7.702444e-7,0.00007562055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.791245,"threshold_uncertainty_score":0.605432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08668587701212378,"score_gpt":0.3545436437538484,"score_spread":0.2678577667417247,"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."}}