{"id":"W2017540850","doi":"10.3136/fstr.14.74","title":"A Dedicated MRI for Food Science and Agriculture","year":2008,"lang":"en","type":"article","venue":"Food Science and Technology Research","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sciencetech (Canada)","funders":"Japan Society for the Promotion of Science; Ministry of Agriculture, Forestry and Fisheries; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Agriculture; Food science; USable; Adipose tissue; Magnetic resonance imaging; Agricultural engineering; Computer science; Biotechnology; Biomedical engineering; Environmental science; Biology; Medicine; Engineering; Radiology; Multimedia; Biochemistry","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":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001325268,0.0000831728,0.0001420337,0.0008821622,0.001783102,0.00002949171,0.0004082274,0.000131845,0.000002024094],"category_scores_gemma":[0.001278078,0.0000592491,0.00000995158,0.006499708,0.01081821,0.0001852274,0.0003470207,0.0003792896,0.000002657723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008293032,"about_ca_system_score_gemma":0.0008597448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002856866,"about_ca_topic_score_gemma":0.000007749672,"domain_scores_codex":[0.9979661,0.000005239893,0.0001169733,0.0005642801,0.0007559269,0.0005915499],"domain_scores_gemma":[0.9971057,0.00005185915,0.00002811937,0.0003452169,0.002262748,0.0002063102],"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":[0.00003631317,0.0001910428,0.002936735,0.00004009049,0.000006981945,0.000009800222,0.0002061259,2.308943e-7,0.8330638,0.1288828,0.006914414,0.02771166],"study_design_scores_gemma":[0.001251002,0.007379655,0.005119827,0.00008553916,0.00001578088,0.001390343,0.001415134,0.0006827163,0.8433257,0.04287049,0.0961936,0.0002702628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608795,0.0009127327,0.003784284,0.03062183,0.00001728095,0.001684058,0.00001852844,0.0003234801,0.001758305],"genre_scores_gemma":[0.9837363,0.0004465382,0.01515929,0.0001008924,0.00002350596,0.0003629189,0.00000150845,0.000005941225,0.0001631541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08927919,"threshold_uncertainty_score":0.9995164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0855961497922652,"score_gpt":0.4140828599770262,"score_spread":0.328486710184761,"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."}}