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Record W1993487170 · doi:10.4141/p03-145

Forage quality of seeded native grasses in the fall season on the Canadian Prairie Provinces

2004· article· en· W1993487170 on OpenAlexvenueaboutno aff
P. G. Jefferson, W. P. McCaughey, K. W. May, Jay Woosaree, Li. McFarlane

Bibliographic record

VenueCanadian Journal of Plant Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsForageNeutral Detergent FiberBiologyAgronomyPhenologyEragrostisDry matterGrazingGrassland

Abstract

fetched live from OpenAlex

There is renewed interest in re-seeding native grasses in the prairie region of western Canada but there is limited information on their forage quality for fall grazing. We evaluated forage quality in early fall of nine native and one introduced grass species for 2 to 4 yr at five locations. The neutral detergent fiber (NDF) was high due to the advanced growth stage of the plants but varied among grass species at all sites . Western wheatgrass, Pascopyrum smithii, exhibited the lowest NDF and highest in vitro organic matter digestibility (IVOMD). Northern wheatgrass, Elymus lanceolatus, exhibited the highest crude protein while western wheatgrass ranked second highest for crude protein. Indiangrass, Sorghastrum nutans, exhibited the highest P and Ca concentrations, while green needle grass, Nasella viridula, and mammoth wildrye, Leymus racemosus, exhibited the lowest concentrations. Acid detergent fiber (ADF) was not highly correlated to IVOMD, presumably due to the mature phenological stage at sampling. Western wheatgrass forage was nutritionally adequate to maintain a dry beef cow during the second trimester of pregnancy. Other species did not “cure on the stem” as had been previously reported and would require supplementary energy and protein to be utilized for fall pastures. Key words: Forage quality, fiber, protein, P, C 4 grasses, C 3 grasses

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)0.002
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 score0.583
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.263
Teacher spread0.214 · 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

Citations35
Published2004
Admission routes2
Has abstractyes

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