{"id":"W4410535262","doi":"10.1007/s11947-025-03886-7","title":"Impact of Extrusion Process on the Macro- and Micro-nutrient in Extruded Food Products: Challenges and Future Trends","year":2025,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Food composition and properties","field":"Nursing","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Extrusion; Macro; Extrusion cooking; Process (computing); Environmental science; Process engineering; Food science; Pulp and paper industry; Chemistry; Materials science; Engineering; Computer science; Metallurgy","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":[],"consensus_categories":[],"category_scores_codex":[0.0001049898,0.0001889507,0.000254916,0.0005383798,0.0001159766,0.00002511108,0.0001466774,0.0002289698,0.000003480307],"category_scores_gemma":[0.00002701104,0.0001137115,0.00002143274,0.0006467763,0.0002864917,0.00006356503,0.00008758161,0.0002349861,1.836146e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001823486,"about_ca_system_score_gemma":0.00001875182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004016192,"about_ca_topic_score_gemma":0.00003518984,"domain_scores_codex":[0.9991105,0.00002855233,0.000202122,0.0003720882,0.00007902279,0.0002077791],"domain_scores_gemma":[0.9995916,0.00002346849,0.00008314018,0.0001973966,0.00008097334,0.00002341797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002948245,0.000457937,0.003276415,0.001292377,0.0001110002,0.000002440104,0.003962408,4.333013e-7,0.07446337,0.007911379,0.00008384597,0.9054902],"study_design_scores_gemma":[0.003268469,0.02629404,0.03625371,0.001113546,0.00007793058,0.0001341423,0.01031146,0.00006062776,0.8798501,0.03610946,0.005944062,0.0005824158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9171731,0.04736884,2.763822e-7,0.03474369,0.00008187558,0.0002066156,0.00001330391,0.00007310855,0.0003391747],"genre_scores_gemma":[0.9975759,0.002186358,0.00003590943,0.00009452296,0.00003562842,0.0000406221,0.000002844438,0.000009807112,0.00001843798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9049077,"threshold_uncertainty_score":0.4637021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545585269410048,"score_gpt":0.2661091595309487,"score_spread":0.2506533068368482,"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."}}