{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000483559,0.0003733784,0.0002236952,0.0002643376,0.0001734057,0.0002738856,0.0001469478,0.0003085489,0.0004638556],"category_scores_gemma":[0.0006165298,0.000113225,0.0002010651,0.0001467991,0.0002347926,0.0004085622,0.0001663492,0.0003170641,0.0001639802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139612,"about_ca_system_score_gemma":0.0002045213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007216924,"about_ca_topic_score_gemma":0.001386613,"domain_scores_codex":[0.9998516,0.00002826398,0.00001164902,0.00004697906,0.00004388478,0.0000175928],"domain_scores_gemma":[0.9997714,0.0000783255,0.00007170764,0.000013862,0.00005101065,0.00001359669],"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":[0.00003906733,0.00001199546,0.0002873309,0.00004770865,0.000006818569,0.00002108726,0.00001038709,0.00007078774,0.9969165,0.00001731727,0.0000095872,0.002561382],"study_design_scores_gemma":[0.000007842933,0.0005994842,0.003129291,0.000008062716,0.00003750685,0.0001038364,0.00002183208,0.001274645,0.9940647,0.00004147792,0.0006993377,0.0000118829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588799,0.00833956,0.03056066,0.0001810878,0.00009632552,0.00008332543,0.000111012,0.0001481857,0.00159996],"genre_scores_gemma":[0.9722533,0.003882288,0.02202725,0.0001970374,0.00004728989,0.00005547979,0.0001339207,0.00004636969,0.001357057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007216924,"threshold_uncertainty_score":0.002557337,"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."}}