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Record W6884635284 · doi:10.1139/cjas-2014-046

Effects of resting perennial pastures during the sensitive pre-dormancy period in western Manitoba: Pasture productivity and beef cattle performance

2015· article· en· W6884635284 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPastureGrazingForagePerennial plantProductivityBeef cattleCattle grazing

Abstract

fetched live from OpenAlex

Abstract. Durunna, O. N., Baron, V., Scott, S. L., Robins, C., Khakbazan, M. and Block, H. C. 2015. Effects of resting perennial pastures during the sensitive pre-dormancy period in western Manitoba: Pasture productivity and beef cattle performance. Can. J. Anim. Sci. 95: 129-141. The objective of this experiment was to determine whether avoiding grazing during the sensitive pre-dormancy period (ca. 6 wk prior to a dormancy-inducing frost) would improve forage production, stand quality, alfalfa persistence and animal productivity in perennial pastures. There were two pasture species (PS), alfalfa-grass (AG) or grass (G), and three grazing phases. Phase I was conventional rotational grazing of all AG and G sections. In Phase II, one half of AG and G was rotationally grazed (conventional treatment, CT) while the other half was not (rested treatment, RT). Resting AG and G in Phase II required transferring RT animals to swath-graze early-seeded cereals. In Phase III, RT animals that swath-grazed in Phase II were moved to graze the rested sections of the pastures while those that grazed the unrested sections (CT animals) were transferred to swath-graze late-seeded cereals. There was no PS (P>0.05) or rest period (P>0.13) effect on total forage yield, carrying capacity, forage disappearance and forage residues. There was no effect (P>0.13) of resting on botanical composition or yield in AG. The current study did not observe significant benefits of resting on pasture yield, botanical composition or animal performance.

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 score0.653
Threshold uncertainty score0.419

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 head0.104
GPT teacher head0.212
Teacher spread0.108 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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