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Record W2011389122 · doi:10.1002/cjce.5450830113

Measurement of pH in Food Systems by Magnetic Resonance Imaging

2008· article· en· W2011389122 on OpenAlexvenueno aff
Stephen D. Evans, Laurie Hall

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryPicklingManganesePhysicsNuclear magnetic resonancePhysical chemistry

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) has been used to measure the nuclear relaxation times of the protons of water containing low concentrations (0.4mM) of the diethylenetriaminepentaaceticacid complex of manganese. Given that the manganese-binding equilibrium is dependent on the local pH, this forms the basis of a physically non-invasive method for quantitation of pH in three dimensions. The basic concepts are briefly described together with specific details of how this MRI method can be used to follow the pickling of garlic immersed in vinegar and of meatballs immersed in vinegar or a tomato sauce. On a eu recours à l'imagerie à résonance magnétique (IRM) pour mesurer les temps de relaxation nucléaire des protons de l'eau contenant de faibles concentrations (0,4 mM) du complexe diéthylènetriaminepentaacéticacide du manganèse. Étant donné que l'équilibre de liaison du manganèse est dépendant du pH local, cette méthode forme la base d'une méthode physiquement non intrusive de quantification du pH en trois dimensions. Les concepts de base sont brièvement décrits ainsi que des détails spécifiques sur la manière dont cette méthode IRM peut être utilisée pour suivre la macération d'ail immergé dans du vinaigre et de boulettes de viande immergées dans du vinaigre ou de la sauce tomate.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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 teacher head, not a consensus.

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

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

Citations6
Published2008
Admission routes1
Has abstractyes

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