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Record W2092107648

Investigation and Modeling of Electrical Stunning Parameters of Turkey Using Response Surface Methodology

2013· article· en· W2092107648 on OpenAlexvenueno aff
Jamel Mejri, Abdelkarim Aydi, Nawzet Bouriga, Safouane Ben Sik Ali, Manef Abderrabba, Mondher Mejri

Bibliographic record

VenueInternational Journal of Biotechnology for Wellness Industries · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
Fundersnot available
KeywordsVoltageVoltPulse (music)StunningAnimal scienceElectrical engineeringEngineeringMedicineBiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Abstract: The aim of this work is to evaluate the effects of electrical stunning parameters of turkey on the quality of steak and stew using the response surface methodology. 1584 turkeys were used for the study. The parameters tested were the voltage and pulse. The responses were the sensory quality of steak and stew. The rejection rate varies between 0.23% (steak female) and 56.06% (female stew). Results show that it is possible to obtain a low rejection rates (< 1% for the steak of male turkey) at a voltage of 50 Volts and pulse of 7. In comparison of pulse, voltage remains the most important factor. The optimal conditions selected for the total output of the process obtained from the analysis of the response surfaces are: voltage, from 45 to 47 Volts; pulse from 6.5 to 7 and voltage from 50 to 52 Volts; pulse from 6.8 to 7, respectively for female and male turkeys. Under these experimental conditions, the rejection rates range from 0.23% to 0.80% and from 11.93% to 13.27% respectively for steak and stew. The results show that the voltage and pulse are both the parameters meaning on the global quality of steak and stew.

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)0.002
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 score0.070
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.198
GPT teacher head0.330
Teacher spread0.131 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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