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Record W2016192805 · doi:10.1002/fsn3.229

Determination of optimum oven cooking procedures for lean beef products

2015· article· en· W2016192805 on OpenAlexaff
Argenis Rodas‐González, I. L. Larsen, B. Uttaro, M. Juárez, Joyce Parslow, J.L. Aalhus

Bibliographic record

VenueFood Science & Nutrition · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsBecton Dickinson (Canada)Agriculture and Agri-Food CanadaUniversity of Manitoba
Fundersnot available
KeywordsBrowningRoastingChemistryFood scienceCooking methodsMathematics

Abstract

fetched live from OpenAlex

In order to determine optimum oven cooking procedures for lean beef, the effects of searing at 232 or 260°C for 0, 10, 20 or 30 min, and roasting at 160 or 135°C on semimembranosus (SM) and longissimus lumborum (LL) muscles were evaluated. In addition, the optimum determined cooking method (oven-seared for 10 min at 232°C and roasted at 135°C) was applied to SM roasts varying in weight from 0.5 to 2.5 kg. Mainly, SM muscles seared for 0 or 10 min at 232°C followed by roast at 135°C had lower cooking loss, higher external browning color, more uniform internal color, and were more tender and flavorful (P < 0.05). Roast weights ≥1 kg had lesser cooking loss, more uniform internal color and tender compared to 0.5 kg (P < 0.05). Consequently, roasting at low temperature without searing is the recommended oven cooking procedure; with best response from muscle roast weight ≥1 kg.

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)0.001
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 score0.020
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.091
GPT teacher head0.294
Teacher spread0.204 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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