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Record W2050206530 · doi:10.1086/505609

Effects of the Spatial Pattern of Leaf Damage on Growth and Reproduction: Whole Plants

2006· article· en· W2050206530 on OpenAlexaff
Germán Ávila‐Sakar, Andrew G. Stephenson

Bibliographic record

VenueInternational Journal of Plant Sciences · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsBiologyPollenAnthesisStamenBotanyHerbivoreFructificationHorticultureAgronomyCultivar

Abstract

fetched live from OpenAlex

Because of differences in their foraging patterns, different herbivores are likely to have different effects on plant fitness. We compared the effects of two contrasting patterns of leaf damage on the growth and reproduction of the wild gourd Cucurbita pepo ssp. texana. Plants were assigned to one of four damage treatments: (1) concentrated (15% damage on every third leaf); (2) dispersed (5% on each leaf); (3) high intensity (15% on each leaf); and (4) control (undamaged). We measured the growth rate of the main vegetative axis of the plants, internode length, the proportions of staminate and pistillate nodes, the proportions of staminate and pistillate buds that reached anthesis, the proportion of pistillate flowers that produced fruit, pollen production and pollen grain size, fruit production, fruit size, number of seeds per fruit, and seed size. Only one trait was significantly affected by the spatial pattern of damage: pollen production per flower increased under the concentrated but not the dispersed damage treatment. Fruit production showed a marginal decrease in the dispersed but not in the concentrated treatment. Very few traits were affected by foliar damage, regardless of the spatial pattern of distribution, suggesting that these plants have a high tolerance to simulated 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.119

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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 designBench or experimental
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
Published2006
Admission routes1
Has abstractyes

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