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Record W4316662329 · doi:10.1111/rda.14319

Rumen‐protected glucose hastens uterine involution and increases numbers of ovarian follicles in early post‐partum dairy cows

2023· article· en· W4316662329 on OpenAlexaff
Reza Karimi, Armin Towhidi, M. Ganjkhanlou, Hamid Ghasemzadeh‐Nava, Sepideh Khoee, John P. Kastelic

Bibliographic record

VenueReproduction in Domestic Animals · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Calgary
FundersUniversity of TehranIran National Science Foundation
KeywordsIce calvingInvolution (esoterism)Transrectal ultrasonographyFollicular phaseAnimal scienceInternal medicineBiologyEndocrinologyLactationPost partumDairy cattleUltrasonographyEstrous cyclePregnancyMedicine

Abstract

fetched live from OpenAlex

The primary objectives were to investigate the effects of feeding a new rumen-protected glucose (RPG) on uterine involution and ovarian follicular dynamics in recently calved dairy cattle. From 4 to 30 days after calving, 16 Holsteins (first to third lactation, mean parity 1.75) were randomly assigned to be fed either a basal diet top-dressed with either 600 g RPG (RPG group) or 600 g of the coating material and glucose (CONT group). Based on transrectal ultrasonography, conducted every 3 days starting 20 days after calving, the interval from calving to complete uterine involution was shorter in RPG versus CONT (27.1 vs. 30.4 days, p < .01). Furthermore, based on transrectal ultrasonography conducted every 2 days, cattle fed RPG had smaller (3.0-4.9 mm) ovarian follicles (2.96 vs. 0.9, p < .001) and more total follicles (5.26 vs. 2.85, p < .01). Feeding RPG had increased serum insulin concentrations (4.59 ± 0.54 vs. 3.13 ± 0.57, p < .05), but had no significant effects on serum glucose concentrations, dry matter intake or milk yield. In conclusion, we inferred that cattle fed RPG had increased glucose turnover that was responsible for higher insulin concentrations, faster uterine involution, and more ovarian follicles.

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)0.001
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 score0.927
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.023
GPT teacher head0.263
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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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