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Record W3169839003 · doi:10.1080/09712119.2021.1933990

Alternative forage feeds for beef cattle in Northwestern Alberta, Canada: forage yield and nutritive value of forage brassicas and forbs

2021· article· en· W3169839003 on OpenAlexafffundabout
Akim Omokanye, Guillermo Hernandez‐Ramirez, H.A. Lardner, Buthaina Al‐Maqtari, K. S. Gill, Alan Lee

Bibliographic record

VenueJournal of Applied Animal Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsNorthwestern PolytechnicUniversity of SaskatchewanUniversity of Alberta
FundersGovernment of Alberta
KeywordsForageAgronomyDry matterBiologyFodderForbGrassland

Abstract

fetched live from OpenAlex

Forbs and forage brassicas are non-traditional forage crops in northwestern Alberta, Canada. Ten forage brassicas (barkant turnips, bayou kale hybrid, daikon radish, collard, inka brand marrowstem kale, malwira turnip rape, purple top turnips, tillage radish, vivant hybrid cross and winfred) and four forbs (buckwheat, chicory, plantain and phacelia) were seeded on 25 May 2018 and 23 May 2019. The above-ground parts of plants were harvested for forage yield and nutritive quality on 15 August 2018 and 29 August 2019. Forage dry matter (DM) yield, crude protein (CP) and total digestible nutrients (TDN) respectively varied from 2953 to 10740 kg DM ha−1, 12.2–23.5% CP and 58.8–77.9% TDN. Some crops had no nitrate (ant-nutritional factor) detected, while six crops (bayou kale cross, chicory, phacelia, plantain, purple top turnips and tillage radish) had nitrate concentrations ranging 0.26–0.56%, considered toxic for beef cattle. In general, the brassicas and forbs investigated produced forage with high nutritional quality. Based on forage DM yield and nitrate level in the forage, the crops with the most attractive forage options that can provide alternative forage feed for beef cattle production from this study would be buckwheat, daikon radish, inka brand marrowstem kale and forage collards.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.049
GPT teacher head0.306
Teacher spread0.257 · 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 designBench or experimental
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

Citations10
Published2021
Admission routes3
Has abstractyes

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