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Record W2097054002 · doi:10.1139/cjb-2013-0246

Sexual dimorphism in leaf nitrogen content but not photosynthetic rates in <i>Sagittaria latifolia</i> (Alismataceae)

2014· article· en· W2097054002 on OpenAlexaffvenue
Veronika L. Wright, Marcel E. Dorken

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsTrent University
Fundersnot available
KeywordsBiologySexual dimorphismPhotosynthesisDioecyBotanyReproductionNitrogenSexual reproductionPopulationBiomass (ecology)EcologyZoologyPollenDemography

Abstract

fetched live from OpenAlex

Sexual dimorphism in reproductive costs between females and males in dioecious plants is associated with divergent morphologies, life histories, and physiologies between the sexes. In Sagittaria latifolia Willd. (Alismataceae), a previous experiment has shown that sexual reproduction imposes asymmetric costs for females vs. males, with greater biomass costs for females and greater nitrogen costs for males. Here we investigate whether sexual dimorphism in nitrogen expenditure for reproduction influences the nitrogen content of leaves under natural conditions and, if so, whether it is associated with differences in photosynthetic rates between the sexes. As expected, we found significantly lower leaf nitrogen content among males compared with females and a positive association between leaf nitrogen content and photosynthetic rates. However, this difference in nitrogen content between the sexes was not associated with different photosynthetic rates for females vs. males. Our study demonstrates that an underlying difference in nitrogen content between the sexes is maintained during flower and fruit production in a natural population, but, at least at the site used for this study, this difference was not associated with divergent photosynthetic rates.

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 score0.810
Threshold uncertainty score0.989

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 head0.055
GPT teacher head0.216
Teacher spread0.162 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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