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Record W2118881623 · doi:10.2134/agronj2005.0171

Carrying Capacity, Utilization, and Weathering of Swathed Whole Plant Barley

2006· article· en· W2118881623 on OpenAlexaff
V. S. Baron, A. C. Dick, D. H. McCartney, J. A. Basarab, E. K. Okine

Bibliographic record

VenueAgronomy Journal · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of AlbertaAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGrazingAgronomyForageHordeum vulgareDry matterCarrying capacityEnvironmental scienceCropAnimal scienceBiologyPoaceaeEcology

Abstract

fetched live from OpenAlex

Winter grazing of swathed whole‐plant small grain crops can reduce costs for beef producers, but little is known about levels of carrying capacity, utilization and weathering losses of nutritive value and their year‐to‐year variability. The objective of the present study was to determine carrying capacity, utilization and weathering losses to nutritive value in relation to beef cow ( Bos taurus ) requirements during winter grazing of swathed whole‐plant spring barley ( Hordeum vulgare L.) in a field‐scale trial. Pregnant beef cows (685 kg wt.) limit‐grazed swathed whole‐plant barley (November–February, 1997–2001) at daily available forage levels approximating 2% of body weight at Lacombe, AB. Carrying capacity was affected by barley yield, utilization rate, and average daily dry matter consumption. Carrying capacities ranged from 481 to 879 cow‐d ha −1 , utilization from 75.5 to 92% and daily dry matter consumption rates ranged from 8.6 to 12.9 kg cow‐d −1 . Weathering losses of nutritive value, as indicated by the difference between that of the standing crop and the mean of grazing season swath, were slight compared to a much larger difference between grazing season swath and residue. Generally, the nutritive requirements for maintenance of beef cows (NRC, 1996) could be readily met by swathed barley.

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 score0.824
Threshold uncertainty score0.122

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 head0.034
GPT teacher head0.209
Teacher spread0.174 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

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