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Record W2131463599 · doi:10.1093/jhered/esp126

Female Reproductive Success Variation in a Pseudotsuga menziesii Seed Orchard as Revealed by Pedigree Reconstruction from a Bulk Seed Collection

2010· article· en· W2131463599 on OpenAlexaff
Y. A. El-Kassaby, Tomáš Funda, Ben Lai

Bibliographic record

VenueJournal of Heredity · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologySeed orchardSelfingGene flowPollenMating systemReproductive successEffective population sizeGenetic variationMatingPopulationBotanyZoologyGeneticsDemographyGene

Abstract

fetched live from OpenAlex

The impact of female reproductive success on the mating system, gene flow, and genetic diversity of the filial generation was studied using a random sample of 801 bulk seed from a 49-clone Pseudotsuga menziesii seed orchard. We used microsatellite DNA fingerprinting and pedigree reconstruction to assign each seed's maternal and paternal parents and directly estimated clonal reproductive success, selfing rate, and the proportion of seed sired by outside pollen sources. Unlike most family array mating system and gene flow studies conducted on natural and experimental populations, which used an equal number of seeds per maternal genotype and thus generating unbiased inferences only on male reproductive success, the random sample we used was a representative of the entire seed crop; therefore, provided a unique opportunity to draw unbiased inferences on both female and male reproductive success variation. Selfing rate and the number of seed sired by outside pollen sources were found to be a function of female fertility variation. This variation also substantially and negatively affected female effective population size. Additionally, the results provided convincing evidence that the use of clone size as a proxy to fertility is questionable and requires further consideration.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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
Published2010
Admission routes1
Has abstractyes

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