{"id":"W4407942594","doi":"10.1016/j.foodchem.2025.143585","title":"Using low-field nuclear magnetic resonance to investigate the effect of composition, mechanical treatments, and storage on the stability of emulsions","year":2025,"lang":"en","type":"article","venue":"Food Chemistry","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Composition (language); Nuclear magnetic resonance; Stability (learning theory); Magnetic field; Field (mathematics); Materials science; Chemistry; Physics; Computer science; Mathematics","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":[0.00006012784,0.00006852439,0.00009923232,0.000004294246,0.0001123633,0.000008048975,0.0001071125,0.00002050572,0.00008340767],"category_scores_gemma":[0.0000103323,0.00004244913,0.00003739932,0.00009489324,0.0000610287,0.000008323153,0.00005076098,0.00008804663,6.370036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001113901,"about_ca_system_score_gemma":0.00001395076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000260432,"about_ca_topic_score_gemma":3.9646e-7,"domain_scores_codex":[0.9996179,0.00002811285,0.0001169085,0.000114506,0.00005265356,0.00006985951],"domain_scores_gemma":[0.9994593,0.0001896748,0.00004166863,0.0002685347,0.00001463804,0.00002616795],"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":[0.00004151231,0.00006791049,0.001075466,0.00003242325,0.00002161187,3.587793e-8,0.0000630914,0.000009106026,0.9903798,0.007706576,0.000124528,0.000477913],"study_design_scores_gemma":[0.000168189,0.0001425002,0.0004129091,0.00007063145,0.00003691012,9.34923e-8,0.00006662679,0.0001942952,0.9973733,0.001381879,0.0001200263,0.00003261071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975882,0.00005265293,0.0001610703,0.000642205,0.000007529562,0.0001902247,0.00005926613,0.000004726979,0.001294092],"genre_scores_gemma":[0.9997693,8.842895e-7,0.0001177444,0.00004610057,0.00001529571,0.00002355615,0.000002730946,0.000003622355,0.0000207673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006993503,"threshold_uncertainty_score":0.1731025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009230845506087923,"score_gpt":0.2812526569107527,"score_spread":0.2720218114046647,"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."}}