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Record W1966275535 · doi:10.4141/cjas09002

Potential of warm-season annual forages and <i>Brassica</i> crops for grazing: A Canadian Review

2009· review· en· W1966275535 on OpenAlexfundvenueaboutno aff
D. H. McCartney, J. Fraser, A. Ohama

Bibliographic record

VenueCanadian Journal of Animal Science · 2009
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
FundersBeef Cattle Research Council
KeywordsGrazingAgronomySecaleBiologyForagePerennial plantBrassica rapaBrassicaAvenaFodderHordeum vulgareSetaria viridisPoaceaeWeed

Abstract

fetched live from OpenAlex

Extension of the grazing season beyond the normal perennial grazing season has been identified as a potential mechanism to reduce the cost of production on Canadian cow-calf operations. This review will provide an overview of the potential use of warm-season and Brassica crops including corn (Zea mays), Golden German foxtail millet (Setaria italica L. Beauv.), kale (Brassica oleracea L.), forage rape (B. napus ssp. biennis L.) and turnip (B. rapa L.). These crops have a high yield potential, but the cost for grazing these crops has not been adequately compared with the cost of grazing oat (Avena sativa L.), barley (Hordeum vulgare L.), and fall rye (Secale cereale L.). There are very few actual animal grazing trials evaluating the economics of using these crops in grazing systems in Canada, and this requires further research.Key words: Corn, millet, sorgum, sudan grass, fodder crops, brassicas

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.286
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations48
Published2009
Admission routes3
Has abstractyes

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