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Heritable variation and genetic correlation of quantitative traits within and between ecotypes of<i>Avena barbata</i>

2008· article· en· W1992080152 on OpenAlexaff
Kyle M. Gardner, Robert G. Latta

Bibliographic record

VenueJournal of Evolutionary Biology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEcotypeBiologyHeritabilityQuantitative trait locusGenetic variationAvenaTransgressive segregationQuantitative geneticsInbred strainDeserts and xeric shrublandsGene–environment interactionAgronomyGeneticsEcologyGenotypeGeneHabitat

Abstract

fetched live from OpenAlex

We examined heritable variation for quantitative traits within and between naturally occurring mesic and xeric ecotypes of the slender wild oat (Avena barbata), and in 188 recombinant inbred lines derived from a cross between the ecotypes. We measured a suite of seedling and adult traits in the greenhouse, as well as performance-related traits in field sites native to the two ecotypes. Although the ecotypes were genetically diverged for most traits, few traits showed significant heritable variation within either ecotype. In contrast, considerable heritable variation was released in the recombinant progeny of the cross, and transgressive segregation was apparent in all traits. Heritabilities were substantially greater in the greenhouse than in the field, and this was associated with an increase in environmental variance in the field, rather than a decrease in genetic variance. Strong genetic correlations were evident among the recombinants, such that 22 measured traits could be well represented by only seven underlying factors, which accounted for 80% of the total variation. The primary axis of variation in the greenhouse described a trade-off between vegetative and reproductive allocation, mediated by the date of first flowering, and fitness was strongly correlated with this trade-off. Other factors in the greenhouse described variation in size and in seedling traits. Lack of correlation among these factors represents the release of multivariate trait variation through recombination. In the field, a separate axis of variation in overall performance was found for each year/site combination. Performance was significantly correlated across field environments, but not significantly correlated between greenhouse and field.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.088

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 head0.041
GPT teacher head0.219
Teacher spread0.178 · 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 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

Citations27
Published2008
Admission routes1
Has abstractyes

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