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Record W4296617585 · doi:10.1093/jas/skac247.163

185 Evaluation of the Chemical Composition of Intermediate Wheatgrass (Thinopyrum Intermedium) Regrowth for Potential Fall Grazing of Beef Cattle

2022· article· en· W4296617585 on OpenAlexaffabout
Patrick Le Heiget, E. J. McGeough, Douglas J. Cattani

Bibliographic record

VenueJournal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGrazingForageAnimal scienceBiologyBeef cattleFertilizerNutrientAgronomyEcology

Abstract

fetched live from OpenAlex

Abstract This study evaluated the chemical composition of 3 intermediate wheatgrass (IWG) treatments compared with a grass/legume control for potential Fall grazing of beef cattle. Treatments were: 1) IWG, pure stand, no fertilizer post establishment (NFERT), 2) IWG, pure stand+50 kg N ha-1 annually post grain harvest (FERT), 3) IWG with Alsike clover (50:50; ACL), 4) Tall fescue/Algonquin alfalfa/Oxley II cicer milkvetch (50:25:25; FORCON). Small plots (4 replicates/treatment) were established in 2019 at 2 sites in Manitoba (Carman and Brandon). The IWG treatments were harvested in August for grain production and the regrowth sampled in October. The FORCON treatment was harvested annually in June and the regrowth stockpiled until October sampling. Forage samples were analyzed for crude protein (CP) and total digestible nutrients (TDN). Data were analyzed using PROC GLIMMIX in SAS, with the fixed effects of treatment and location, with replicate within location nested as a random effect. For CP, in 2020 at Carman, FORCON was less (10.0%) than the IWG treatments, which, in turn, did not differ (mean 19.5%). At Brandon, the opposite response was observed with FORCON greater (11.5%) than the IWG treatments (mean 8.7%). In 2021 at Carman, FERT had greater CP (14.3%) than all other treatments, which, in turn, did not differ (mean 9.9%). At Brandon, FORCON was greatest in CP (15.8%), with ACL and NFERT least (mean 8.9%) and FERT intermediate. In terms of TDN, in 2020 at both sites, FORCON was less (mean 55.3%) than all IWG treatments, which, in turn, did not differ (mean 64.4%). In 2021 at Carman, FERT and FORCON were greater (mean 67.0%) than NFERT and ACL (mean 59.5%). In Brandon, FERT was greater than ACL and NFERT, with FORCON at an intermediate value. By the second production year, the fertility treatment significantly increased the nutritive value of the IWG treatments.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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