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Record W2802424447 · doi:10.1139/cjas-2017-0098

Assessment of ergot (<i>Claviceps purpurea</i>) exposure in pregnant and postpartum beef cows

2018· article· en· W2802424447 on OpenAlexafffundvenue
T. Grusie, Vanessa Cowan, Jaswant Singh, James McKinnon, Barry Blakley

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and fungal interactions
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - SaskatchewanUniversity of Saskatchewan
KeywordsClaviceps purpureaAnimal scienceCorpus luteumOvulationProlactinPregnancyPostpartum periodBeef cattleChemistryBiologyEndocrinologyHormoneBiochemistry

Abstract

fetched live from OpenAlex

Cows were fed ration for 9 wk containing 5, 48, 201, and 822 μg kg −1 ergot alkaloids. The objective was to evaluate the impact of ergot consumption in beef cow–calf operations. Ergot alkaloids up to 822 μg kg −1 did not alter the weight of peripartum and postpartum beef cows (P = 0.93) or nursing calves (P = 0.08), rectal temperature (P = 0.16), or plasma prolactin concentrations (P = 0.30) at moderate ambient temperatures. Ergot did not influence the time (&gt;1 ng mL −1 ; P = 0.79) or the progesterone concentration (P = 0.38) at the time of first postpartum rise or the size of the first (14 ± 0.6 mm; P = 0.40) and second (13 ± 0.5 mm; P = 0.41) follicles to ovulate. The maximum size of the first postpartum corpus luteum (CL) was 4 mm larger in the 822 μg kg −1 ergot group compared with the control (P = 0.03) for the first ovulation post partum, but not for the second (P = 0.11). There was no effect of ergot exposure on the number of days until the appearance of the first (43 ± 4 d; P = 0.95) or second (52 ± 4 d; P = 0.98) CL post partum. Ergot alkaloid concentrations up to 822 μg kg −1 did not affect pregnancy rates (X 2 = 0.36). In conclusion, ergot alkaloid exposure for 9 wk to concentrations as high as 822 μg kg −1 did not alter performance in pregnant and postpartum beef cattle at moderate ambient temperatures.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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

Citations7
Published2018
Admission routes3
Has abstractyes

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