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Modelling of cooking‐cooling processes for meat and poultry products

2008· article· en· W2035193527 on OpenAlexaff
Michèle Marcotte, Cuiren R. Chen, S Grabowski, Hosahalli S. Ramaswamy

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMcGill UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsThermal diffusivityHeat transferVolume (thermodynamics)Sensitivity (control systems)ThermalEnergy consumptionThermodynamicsMaterials scienceMathematicsMechanicsProcess engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Summary Traditional cooking‐cooling of processed meat and poultry products is industrially carried out in smokehouses or autoclaves. A mathematical model was developed to simulate these operations. Equations, describing heat transfer and thermal destruction of micro‐organisms and quality characteristics, were solved numerically. The model was validated experimentally for heat transfer and energy consumption and was used to perform a sensitivity analysis. Input variables were: process time (PT), smokehouse temperature ( T SH ), bologna size (diameter, D and height, H ), surface heat transfer coefficients ( h heat and h cool ), product thermal diffusivity ( α heat and α cool ). Output variables were: product core temperature ( T c ), core and volume‐average lethality ( P cm and P vm ) and cook ( C c and C v ) values as well as surface ( Q s ) and volume‐average ( Q v ) quality retention, total specific energy consumption (En) and energy efficiency (Ce). Multiple linear regression models were developed to predict C c and C v from five inputs and used to obtain acceptable deviation ranges.

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)0.001
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 score0.016
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.282
Teacher spread0.196 · 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

Citations18
Published2008
Admission routes1
Has abstractyes

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