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Record W4283713951 · doi:10.1111/evo.14544

Mating and fitness consequences of variation in male allocation in a wind‐pollinated plant

2022· article· en· W4283713951 on OpenAlexaff
Abrar A. Aljiboury, Jannice Friedman

Bibliographic record

VenueEvolution · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiologySex allocationMatingAmbrosia artemisiifoliaReproductive successAnemophilyMating systemPollinationEcologyRagweedDemographyPollenOffspringGeneticsPopulationPollinator

Abstract

fetched live from OpenAlex

In hermaphrodites, the allocation of resources to each sex function can influence fitness through mating success. A prediction that arises from sex allocation theory is that in wind-pollinated plants, male fitness should increase linearly with investment of resources into male function but there have been few empirical tests of this prediction. In a field experiment, we experimentally manipulated allocation to male function in Ambrosia artemisiifolia (common ragweed) and measured mating success in contrasting phenotypes using genetic markers. We investigated the effects of morphological traits and flowering phenology on male siring success, and on the diversity of mates. Our results provide evidence for a linear relation between allocation to male function, mating, and fitness. We find earlier onset of male flowering time increases reproductive success, whereas later flowering increases the probability of mating with diverse individuals. Our study is among the first empirical tests of the prediction of linear male fitness returns in wind-pollinated plants and emphasizes the importance of a large investment into male function by wind-pollinated plants and mating consequences of temporal variation in sex allocation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.364

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 head0.031
GPT teacher head0.203
Teacher spread0.172 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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