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Record W2117927899

EFFECTS OF VARYING LEVELS OF DCAD WITH TWO LEVELS OF MG AND K ON ACID BASE STATUS, MG METABOLISM AND PRODUCTIVE PERFORMANCE OF BEETAL GOATS

2014· article· en· W2117927899 on OpenAlexaff
Umer Farooq, T. N. Pasha, M. A. Jabbar, Muhammad Abdullah

Bibliographic record

VenueThe Journal of Animal and Plant Sciences · 2014
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnimal scienceLactationChemistryFactorial experimentUrineFeed conversion ratioExcretionBiologyBody weightEndocrinologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Thirty-six Beetal goats in early lactation were used in a 6-wk experiment with a 3 x 2 x 2 factorial arrangement of treatments. The objective was to reveal the effects of three levels of DCAD ( -15, 2.5 and 20mEq/100g of feed DM) with two levels of K (1.35 and 2.0% of feed DM) and two levels of Mg (0. 37 and 0.74%) in diets on productive performance. Increasing DCAD levels in diets significantly increased DMI, milk yield and milk fat percentage. Moreover, increasing K levels in diets increased milk yield of goats. However, increasing Mg levels in diets from 0.37% to 0.74% of feed DM negatively influenced the DMI intake, DM digestibility, milk yield and milk protein contents, as all the traits were reduced by increasing Mg levels. A linear increase in pH and HCO 3 − contents of blood and urine by increasing DCAD levels in diets evidenced a positive alteration in acid base status of the animals. However, K and Mg l evels of diets showed no effect on same traits. Moreover, increasing K levels of diets reduced the Mg absorption. Similarly, higher Mg absorption, retention and balance were observed when added Mg was increased in diets. Overall, increasing DCAD levels in diets improved DMI and milk yield (3.5 and 11.1%, respectively), however, increasing Mg levels in diets showed negative effects on productive performance of Beetal goats.

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 score0.567
Threshold uncertainty score0.221

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 studies0.0000.001
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 head0.029
GPT teacher head0.268
Teacher spread0.240 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

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