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Record W2077064221 · doi:10.4141/a00-067

Assessment of tail-end dehulled canola meal for use in broiler diets

2001· article· en· W2077064221 on OpenAlexafffundvenue
W. D. Clark, H.L. Classen, Rex W. Newkirk

Bibliographic record

VenueCanadian Journal of Animal Science · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Saskatchewan
FundersCanola Council of Canada
KeywordsCanolaBroilerMealFood scienceAmino acidBiologyNutrientFeed conversion ratioAnimal scienceBody weightBiochemistry

Abstract

fetched live from OpenAlex

The value of tail-end partially dehulled canola meal (DCM) was assessed in comparison to the conventional canola meal (CCM) from which it was derived using broiler chickens. CCM obtained from five crushing plants underwent the partial-dehulling. Nutrient retention was determined using 33-d-old broiler chickens and a 21 -d growth study was also conducted. DCMs contained a higher concentration of crude protein and amino acids and the utilization of energy and amino acids was improved. Energy utilization was also affected by crushing plant and there was an interaction between plant and meal type, suggesting that the dehulling was not uniform for meals obtained from the five plants. The DCM had higher digestibilities than the CCM (for 10 amino acids), but there were also plant effects for 7 amino acids. In the second experiment, feed intake, weight gain and mortality levels were not affected by tail end dehulling, but feed efficiency was. The similarity in chick performance and health between the chicks fed DCM and CCM indicates that there was no apparent concentration of anti-nutritional factors. In conclusion, although variability between crushing plants is a concern, tail-end dehulling has potential to increase the quality and marketability of canola meal for poultry diets. Key words: Dehulled canola meal, broiler chickens, metabolizable energy, amino acids, digestibility, growth

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.051
GPT teacher head0.277
Teacher spread0.226 · 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 designObservational
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

Citations24
Published2001
Admission routes3
Has abstractyes

Explore more

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