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Record W4387101959 · doi:10.1098/rsos.231117

Male reproductive success is not strongly affected by phenological changes in mate availability in monoecious <i>Sagittaria latifolia</i>

2023· article· en· W4387101959 on OpenAlexafffund
Allison Kwok, Samantha Stephens, Marcel E. Dorken

Bibliographic record

VenueRoyal Society Open Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsPlant reproductive morphologyPhenologyBiologyBotanyReproductive successDemographyPopulation

Abstract

fetched live from OpenAlex

Many plants express their female and male sex roles at different times (dichogamy), with important consequences for mating. Dichogamy can yield mate limitation via biased floral sex ratios, particularly at the beginning and end of the flowering season when many plants simultaneously function as the same sex. This form of mate limitation should be reduced if plants adjust their allocations to female versus male sex functions in a manner that tracks seasonal variability in mating opportunities. For example, under protogyny (i.e. dichogamy with female function expressed first) plants with male-biased sex expression should have enhanced mating opportunities early in the flowering season as other plants begin to flower (in female sex phase). We quantified seasonal changes in sex allocation, patterns of mate availability and realized siring success in a population of protogynous Sagittaria latifolia . Our results were consistent with previous findings that seasonal changes in sex allocation should compensate for lost mating opportunities under the temporally variable mating environments caused by dichogamy. However, patterns of siring success in the population were inconsistent with this interpretation. We suggest that realized siring success might depend more strongly on spatial than on temporal aspects of mate availability.

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)0.002
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 score0.257
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.267
Teacher spread0.221 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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