{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001406439,0.001497251,0.001115175,0.0018194,0.00101528,0.001168406,0.001606698,0.001632891,0.08525024],"category_scores_gemma":[0.00181453,0.0005957152,0.0006843852,0.0008113991,0.0006357832,0.001499558,0.002548751,0.001918105,0.0431296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006700676,"about_ca_system_score_gemma":0.001107843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002939566,"about_ca_topic_score_gemma":0.0003606892,"domain_scores_codex":[0.9991491,0.0001116772,0.000049002,0.0002937774,0.0003097175,0.00008683663],"domain_scores_gemma":[0.9979485,0.0002719966,0.0001086418,0.0005635059,0.0007244877,0.0003829242],"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":[0.0006142388,0.0001256072,0.0006228356,0.0009440967,0.00006306983,0.000707485,0.0002661716,0.0003773138,0.4815534,0.03865543,0.09580529,0.380265],"study_design_scores_gemma":[0.0001081417,0.0004572251,0.001283223,0.0001362778,0.00007654817,0.003147434,0.00004571344,0.002294131,0.05813259,0.002838695,0.9314221,0.00005795194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02158847,0.01811863,0.731401,0.005924257,0.01325858,0.001813143,0.003026112,0.02361468,0.1812551],"genre_scores_gemma":[0.09025614,0.007212702,0.5914938,0.004240648,0.003360604,0.002974095,0.003854493,0.002617441,0.2939901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08525024,"threshold_uncertainty_score":0.2851904,"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."}}