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Record W2895265945 · doi:10.1017/s1751731118002318

Effect of chromium supplementation on growth performance, meal pattern, metabolic and antioxidant status and insulin sensitivity of summer-exposed weaned dairy calves

2018· article· en· W2895265945 on OpenAlexaff
F. Mousavi, S. Karimi-Dehkordi, S. Kargar, Mohtasham Ghaffari

Bibliographic record

Venueanimal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsUniversity of Alberta
FundersEuropean Food Safety Authority
KeywordsAnimal scienceInsulinStarterWeaningDairy cattleFeed conversion ratioFecesBlood sugarMealBiologyEndocrinologyInternal medicineChemistryMedicineFood scienceDiabetes mellitusBody weight

Abstract

fetched live from OpenAlex

Stressful situations may result in serum chromium (Cr) depletion with increased urinary excretion of the mineral and increased Cr requirements. The objective of this study was to investigate the effects of Cr supplementation on growth performance, feeding behavior, blood metabolites and hormones, indicators of oxidative stress and glucose-insulin kinetics of summer-exposed weaned dairy calves. In total, 48 Holstein female calves (63 days of age; 77.0±1.45 kg of BW) were assigned randomly to one of two treatments: (1) a control group with no supplemental Cr (Cr-), and (2) a supplemental Cr group (Cr+) to supply 0.05 mg Cr as Cr-methionine/kg of BW0.75. Chromium was provided in the starter feed and adjusted weekly based on BW over the experimental period. All calves were on experiment for 4 weeks after weaning. The average maximum temperature-humidity index was 76.1 units during the study period, indicating a mild degree of environmental heat load. Results indicated that in summer-exposed dairy calves, increased dietary Cr provision tended to decrease fecal score, tended to change rumination pattern, increased antioxidant capacity by increasing serum concentration of catalase, but had no effects on growth performance, metabolic status or peripheral glucose and insulin metabolism.

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 score0.141
Threshold uncertainty score0.382

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 head0.007
GPT teacher head0.233
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 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

Citations22
Published2018
Admission routes1
Has abstractyes

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