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Record W4386102994 · doi:10.3168/jds.2022-23065

The effect of progesterone concentrations during superovulation of Holstein heifers in a randomized trial

2023· article· en· W4386102994 on OpenAlexafffund
J.C.S. Marques, J P O Maciel, J. Denis-Robichaud, Amanda Bega, Sarah R. Moore, Marc‐André Sirard, Christine F. Baes, R.L.A. Cerri

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of GuelphUniversité LavalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGenome Canada
KeywordsEstrous cycleOvulationArtificial inseminationFollicular phaseAnimal scienceEmbryoInseminationBiologyAndrologyInternal medicineEstrus synchronizationEndocrinologyPregnancyMedicineHormone

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the effect of different progesterone (P4) concentrations during the follicular growth on the intensity of estrous expression, ovarian response to the superovulatory treatment, and embryo production and quality in superovulated heifers. A total of 63 Holstein heifers were randomly assigned into 2 experimental groups: Low P4 (n = 31) and High P4 (n = 32). Animals received a pre-synchronization protocol followed by a protocol of superovulation that included the allocated P4 treatment. Activity was monitored continuously by an automated activity monitor, and estrus characteristics (maximum intensity and duration) were recorded. Embryo collection was performed 7 d post artificial insemination (AI). Embryos were counted and graded from good or excellent (1) to degenerated (4). The outcomes of interest were: number and diameter of follicles at the time of AI, ovulation success (confirmed 7 d post-AI), time to estrus event, maximum intensity and duration of estrus, number and quality of embryos. Data were analyzed according to the type of outcome variable using logistic, linear, or Poisson regression models. A total of 105 embryos (High P4: n = 42; Low P4: n = 63) were graded for quality. Different P4 levels did not affect the maximum intensity (High P4 = 497.8 ± 23.9%; Low P4 = 542.2 ± 23.5%) or the duration (High P4 = 13.5 ± 1.5 h; Low P4 = 14.3 ± 1.4 h) of estrus. Heifers in the High P4 treatment had greater number of follicles at time of AI (High P4 = 16.6 ± 1.6 follicles; Low P4 = 13.9 ± 1.2 follicles), but with smaller diameter (High P4 = 11.3 ± 0.1 mm; Low P4 = 12.0 ± 0.1 mm) compared with Low P4. High P4 heifers tended to have better embryo quality compared with Low P4 heifers (odds ratio = 1.98; 95% CI = 0.90-4.35). High P4 heifers had less embryos than Low P4 heifers, but this was modified by the CIDR (intravaginal implant of P4) removal to estrus interval (interval 0-21 h: mean ratio = 1.15, 95% CI = 0.42-1.87; interval 22-46 h: mean ratio = 0.58, 95% CI = 0.27-0.96). Although estrous expression was not associated with embryo quality, as the duration and the maximum intensity of estrous expression increased, the number of embryos recovered 7 d post-AI increased (duration: mean ratio = 1.04; 95% CI = 1.03-1.05; maximum intensity: mean ratio = 1.50; 95% CI = 1.42-1.58). In conclusion, P4 during the follicular growth, and intensity of estrus, are playing a role in regulating the quality and the number of embryos produced by superovulated heifers. This study was supported by contributions from Resilient Dairy Genome Project and the Natural Sciences and Engineering Research Council.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations4
Published2023
Admission routes2
Has abstractyes

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