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Record W2150572528 · doi:10.1139/b06-137

Variable extrafloral nectary expression and its consequences in quaking aspen

2007· article· en· W2150572528 on OpenAlexvenueno aff
Patricia Doak, Diane Wagner, Adam Watson

Bibliographic record

VenueCanadian Journal of Botany · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersDivision of Environmental Biology
KeywordsHerbivoreBiologyNectarBotanyShootInsectPollen

Abstract

fetched live from OpenAlex

Extrafloral nectaries (EFNs) are secretory glands most commonly linked to defensive mutualisms. Both a plant’s need for defense and the strength of defense provided by mutualists will vary with plant condition and local insect community. Thus, the benefit of EFNs may vary spatially and temporally. However, little attention has been paid to natural variation in the presence and abundance of EFNs within and among individuals of the same species. Quaking aspen, Populus tremuloides Michx., bear EFNs on a subset of their leaves. Here, we describe patterns of EFN expression on shoots within ramets, among ramets, and among putative clones in interior Alaska. We also examine the relationship between EFN presence and herbivory by both the very abundant aspen leaf miner, Phyllocnistis populiella Chambers, and less common chewing herbivores. The proportion of leaves bearing EFNs varied from 33% to 87% among distinct aspen stands. Within stands, short (1–2 m height) ramets had higher EFN frequency than their taller (>4 m) neighbors. Patterns of herbivory also differed between short and tall ramets. Compared with leaves without EFNs, those with EFNs suffered less mining damage on short ramets but slightly higher damage on tall ramets. Tall ramets suffered more chewing damage than short ramets, but this damage was unrelated to the presence of EFNs. Our results suggest that variable EFN expression may be explained by variation in the benefits of EFNs. Leaves with EFNs on short ramets benefit through reduction in leaf mining, but this benefit does not extend to tall ramets or other forms of herbivory.

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 score0.848
Threshold uncertainty score0.731

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 head0.043
GPT teacher head0.213
Teacher spread0.170 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

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