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Record W2017540850 · doi:10.3136/fstr.14.74

A Dedicated MRI for Food Science and Agriculture

2008· article· en· W2017540850 on OpenAlexaff
Mika Koizumi, Shigehiro Naito, Nobuaki Ishida, Tomoyuki Haishi, Hiromi Kano

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.085
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0850.043

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2008
Admission routes1
Has abstractyes

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