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Record W7044434169

Yield and Quality of Cereal and Cereal-Pea Companion Crops and Their Effect on Alfalfa Establishment

2024· article· en· W7044434169 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsTriticaleForageCropYield (engineering)Field experimentFodder
DOInot available

Abstract

fetched live from OpenAlex

This study was carried out to determine the effect of management practices (harvest maturity, cereal species, mixtures with field peas (Pisum sativum)) on the yield and quality of cereal companion crops harvested for forage and the impact of those practices on subsequent alfalfa (Medicago sativa) yields. Replicated factorial experiments were conducted at three locations in northern Ontario from 1993 to 1995. Companion crop forage yields were increased and quality decreased by harvesting as the heads emerged as compared to the late boot stage. Triticale (X Triticosecale) was lower yielding than either oats (Avena sativa) or barley (Hordeum vulgare). Triticale quality was higher primarily due to a higher content of underseeded alfalfa in the harvested forage. Adding peas to cereal companion crops increased crude protein by 2 to 5 percentage units and decreased NDF by 3 to 7 percentage units. Companion crop management usually had no effect on followingyear alfalfa yields, except when cereal regrowth was unusually vigorous. Recommendations for companion crop management specific to the intended end-use can now be formulated.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.030
GPT teacher head0.226
Teacher spread0.197 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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