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Half‐sib family selection for forage yield in orchardgrass

2002· article· en· W2004219129 on OpenAlexaff
Michael D. Casler, S. L. Fales, A. R. McElroy, M. H. Hall, L. D. Hoffman, D. J. Undersander, K. T. Leath

Bibliographic record

VenuePlant Breeding · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsForageBiologyGrazingHayAgronomyYield (engineering)Selection (genetic algorithm)PopulationDactylis glomerataGenetic gainGenetic variationPoaceaeDemography

Abstract

fetched live from OpenAlex

Abstract Genetic variation for forage yield of orchardgrass is abundant, but there are few reports of progress from selection for increased forage yield. The objective of this study was to estimate direct effects of selection from one cycle of half‐sib family selection for forage yield in orchardgrass. Eleven selected populations were compared with their parent populations within three maturity groups. Populations were evaluated under hay management at three locations and management‐intensive rotational grazing at two locations. Nine of the 11 selected populations differed, by an average of 7.4%, from their parent population in forage yield. Nine of the selected populations also showed changes in Drechslera leafspot reaction, all indicating a negative genetic correlation with forage yield. Selection for high forage yield tended to result in greater ground cover and later relative maturity. However, changes in net herbage accumulation (NHA) under rotational grazing were generally not significant and were uncorrelated with changes in forage yield, indicating that forage yield of hay plots is not correlated with the NHA of grazed plots. Although genetic gains in forage yield measured under hay management were very favourable relative to other reports from the literature, the lack of correlated progress under grazing management indicates that directed selection for NHA of orchardgrass should be conducted under grazing management.

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)0.000
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 score0.920
Threshold uncertainty score0.144

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.087
GPT teacher head0.230
Teacher spread0.143 · 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

Citations19
Published2002
Admission routes1
Has abstractyes

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