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Record W4318757260 · doi:10.1016/j.foodres.2023.112566

Effects of electric and magnetic field on freezing characteristics of gel model food

2023· article· en· W4318757260 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 Research International · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryMagnetic fieldIce crystalsElectric fieldControl sampleFluorescencePhase (matter)Analytical Chemistry (journal)Nuclear magnetic resonanceChromatographyFood scienceOptics

Abstract

fetched live from OpenAlex

The novel freezing technologies including electrostatic field assisted freezing (EF), static magnetic field assisted freezing (MF), electrostatic field combined with static magnetic field assisted freezing (EMF) were conducted on model food to facilitate comparing their application effect. The results show that the effect of EMF treatment was best, which significantly changed the freezing parameters of the sample. Compared with the control, the phase transition time and total freezing time were shortened by 17.2% and 10.5%, respectively; the proportion of the sample free water content detected by low-field nuclear magnetic resonance was significantly decreased; the gel strength and hardness were significantly improved; the protein secondary and tertiary structures were better maintained; the ice crystal area was reduced by 49.28%. Inverted fluorescence and scanning electron microscopic results indicated that the gel structure of EMF treatment samples was better than MF and EF. MF was less effective in maintaining the quality of frozen gel model.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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