MétaCan
Menu
Back to cohort
Record W2017540850 · doi:10.3136/fstr.14.74

A Dedicated MRI for Food Science and Agriculture

2008· article· en· W2017540850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood Science and Technology Research · 2008
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSciencetech (Canada)
FundersJapan Society for the Promotion of ScienceMinistry of Agriculture, Forestry and FisheriesMinistry of Education, Culture, Sports, Science and Technology
KeywordsAgricultureFood scienceUSableAdipose tissueMagnetic resonance imagingAgricultural engineeringComputer scienceBiotechnologyBiomedical engineeringEnvironmental scienceBiologyMedicineEngineeringRadiologyMultimediaBiochemistry

Abstract

fetched live from OpenAlex

A dedicated magnetic resonance imaging (MRI) apparatus that is small, lightweight, and usable in an ordinary research room was devised for developmental research and quality estimation of foods and agricultural products. The thawing processes of frozen margarine and meats were traced, the distributions of oils in adipose tissue (fat) and water in muscle tissue for pork and beef were distinctively visualised, the oil-accumulating tissues in seeds and the sticky materials on surface of fermented soybeans (natto) were characterised, and the three-dimensional organisation of the fine vasculature in fruits was visualised by the apparatus. The proton-specified MRI was easy to operate and provided well depicted images of internal structures, the distribution and mobility of water and oils, and susceptibility differences inside materials, demonstrating that the devised machine is useful for food and agricultural research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0020.011
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.414
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it