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Nutritional Evaluation of Pulses and Soybean Meal in Dogs

2016· article· en· W2499888288 on OpenAlexaboutno aff
J.K. Dhaliwal, A.P.S. Sethi, S.S. Sikka

Bibliographic record

VenueIndian Journal of Animal Nutrition · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsVeterinary medicineMealSoybean mealObesityMedicineBiologyAnimal scienceInternal medicine

Abstract

fetched live from OpenAlex

Six adult Labrador dogs were used in this experiment to evaluate total tract apparent nutrient digestibility and energy values of some protein feedstuffs. Seven digestibility trials were conducted. The 30% of basal diet was replaced with test ingredient which included one conventional feed ingredient i.e. soybean (Glycine max) meal (SBM) and five pulses (black-eyed pea or roongi (Vigna unguiculata), red lentil or masoor (Lens culinaris) dhuli, moong (Vigna radiata) dhuli, split bengal gram or chana (Cicer arietinum) dal and split black gram or urad (Vigna mungo) dhuli dal). The prepared mash diets were cooked and then fed to each dog as per their body weight and nutrient requirement. The difference/substitution method was used to estimate total tract apparent nutrient digestibility and energy values of these six protein ingredients. The DM, OM, CP, EE digestibility of masoor dhuli dal was significantly (P<0.05) higher than all other protein ingredients. The digestibility of DM, CP, EE, CF and DE in SBM was similar to that in masoor dhuli dal, however, ME value of masoor dhuli dal was higher (P<0.05) than that of SBM. The ME value of chana dal and mung dhuli dal was similar to that of SBM. It was concluded that masoor dhuli dal could be used as an effective protein source in dog diets.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.268
Teacher spread0.231 · 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
Published2016
Admission routes1
Has abstractyes

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