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HOW DEPRESSED? ESTIMATES OF INBREEDING EFFECTS DURING SEED DEVELOPMENT DEPEND ON REPRODUCTIVE CONDITIONS

2011· article· en· W1577520719 on OpenAlexafffund
Lawrence D. Harder, Nina Hobbhahn, Shane A. Richards

Bibliographic record

VenueEvolution · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInbreeding depressionBiologyOvulePollenMating systemOutcrossingOutbreeding depressionInbreedingPollinationPopulationReproductive successEcologyMatingDemography

Abstract

fetched live from OpenAlex

Inbreeding depression can reduce the performance of offspring produced by mating between relatives, with consequences for population dynamics and sexual-system evolution. In flowering plants, inbreeding depression commonly acts most intensely during seed development. This predispersal component is typically estimated by comparing seed production following exclusive self- and cross-pollination, but such estimates are unbiased only if seed production is limited by ovule availability, rather than by pollen receipt or seed-development resources. To overcome this problem, we propose experimental and statistical methods based on a model of ovule fertilization and seed development that accounts for differential fertilization by self- and cross-pollen, limited ovule viability or receptivity, differential survival of self- and cross-zygotes and limited resource availability. Simulations illustrate that the proposed methods eliminate bias in estimated predispersal inbreeding depression caused by pollen limitation and can improve estimates under resource limitation. Application of these methods to two orchid species further demonstrates their utility in identifying and estimating diverse influences on reproductive performance under typical conditions. Although our theoretical results raise questions about the reported intensity of predispersal inbreeding depression, our proposed methods guard against bias while also providing insight into plant reproduction.

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 score0.618
Threshold uncertainty score0.174

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 head0.046
GPT teacher head0.196
Teacher spread0.151 · 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

Citations12
Published2011
Admission routes2
Has abstractyes

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