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Record W2137970405 · doi:10.1139/b08-125

Compensatory mechanisms for reproductive costs in the dioecious tree <i>Salix integra</i>

2009· article· en· W2137970405 on OpenAlexvenueno aff
Munetaka Tozawa, Naoto Ueno, Kenji Seiwa

Bibliographic record

VenueBotany · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyLeafyDioecyShootPhotosynthesisBotanyBiomass (ecology)ReproductionBiomass partitioningSexual reproductionCompensatory growth (organ)AgronomyEcologyPollen

Abstract

fetched live from OpenAlex

In dioecious plants, females often incur greater reproductive costs than males, owing to the production of seed. This has led to evolution of cost-compensatory mechanisms in females. In trees, however, reproductive cost and compensatory mechanisms are not always detectable, probably because costs and compensation traits change with time, plant size, and modular level (i.e., individual shoots and whole plants). Herein we investigated sex-specific reproductive allocation, growth, and carbon acquisition mechanisms at different hierarchical levels in a dioecious tree, Salix integra Thunb. At both shoot and whole-plant levels, females invested more resources into reproduction than males, but without an associated reduction in vegetative growth, suggesting compensatory mechanisms in females. Females had greater biomass allocation to photosynthetic organs and higher photosynthetic rates than males. Although photosynthetic rates decreased with age, higher shoot turnover in females maintained higher productivity by consistently locating new leaves in favourable light. Females had larger areas of leafy bracts beneath peduncles, suggesting a reduction in the translocation distance of the assimilation to mature seeds. Females meet greater reproductive costs not by reducing growth, but by increasing the carbon uptake ability of different modular units in well-illuminated microenvironments.

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 score0.918
Threshold uncertainty score0.161

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 head0.035
GPT teacher head0.229
Teacher spread0.194 · 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

Citations30
Published2009
Admission routes1
Has abstractyes

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