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Path analysis of natural selection via survival and fecundity across contrasting environments in <i>Avena barbata</i>

2009· article· en· W1988996814 on OpenAlexafffund
Robert G. Latta, Christy M. McCain

Bibliographic record

VenueJournal of Evolutionary Biology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyFecundityAvenaNatural selectionSelection (genetic algorithm)Path analysis (statistics)EcologyEvolutionary biologyDemographyPopulationStatistics

Abstract

fetched live from OpenAlex

We employed path analysis to analyse natural selection through two major fitness components in each of three contrasting environments. Using a randomized block design, 188 Recombinant Inbred Lines (RILs) derived from a cross between contrasting ecotypes of Avena barbata were planted in common gardens in the greenhouse, and in two field sites typical of each ecotype's native habitat. Individuals were monitored for germination phenology, early growth, survival, final size, flowering phenology, reproductive allocation, fecundity and lifetime reproductive success. The variance/covariance matrix of the RIL (genotype) means was fit to a path model in which total fitness was made up of survival and fecundity (of survivors) components. In the greenhouse, all fitness variation was determined by fecundity variation (with no mortality), which was itself primarily determined by reproductive allocation mediated by date of first flowering. By contrast, in the field, early growth was the major determinant of survival, and final size was the major determinant of fecundity. Both components of fitness affected lifetime reproductive success equally in the field. Thus the major difference between greenhouse and field seems to be a shift from selection on allocation patterns in adults, to selection on resource acquisition, especially at earlier life stages. The pattern of selection was similar in the two field sites, despite the contrasting environments.

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 score0.376
Threshold uncertainty score0.123

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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
Published2009
Admission routes2
Has abstractyes

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