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Record W2077295153 · doi:10.1086/656511

Pollinators, Herbivores, and the Maintenance of Flower Color Variation: A Case Study with<i>Lobelia siphilitica</i>

2010· article· en· W2077295153 on OpenAlexaff
Christina M. Caruso, Stephanie L. Scott, Julie C. Wray, Catherine A. Walsh

Bibliographic record

VenueInternational Journal of Plant Sciences · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPollinatorBiologyHuePollinationSelection (genetic algorithm)PredationBotanySeed predationHerbivorePredatorEcologyPollenPopulationSeed dispersal

Abstract

fetched live from OpenAlex

Conflicting selection by pollinators and herbivores is thought to be an important mechanism maintaining variation in flower color within plant populations. However, evidence for this mechanism is lacking because selection and the agents of selection on flower color have rarely been estimated. We estimated selection by pollinators and a predispersal seed predator on the three fundamental components of color (brightness, chroma, and hue) of Lobelia siphilitica flowers. We compared phenotypic selection on flowers of supplemental hand- versus open-pollinated plants to infer whether pollinators were an agent of selection on color. We compared attacked and unattacked plants to infer whether the seed predator was an agent of selection on color. Selection on brightness, but not chroma or hue, differed significantly between both pollination treatments and predation categories. Both pollinators and the seed predator exerted selection for less bright flowers, suggesting that they do not cause conflicting selection on flower color. However, we also detected phenotypic selection for brighter flowers that was not caused by pollinators or by the seed predator. Consequently, pollinators and herbivores are not sufficient to generate conflicting selection that could contribute to the maintenance of flower color variation in L. siphilitica.

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)0.001
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 score0.190
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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

Citations72
Published2010
Admission routes1
Has abstractyes

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