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Ecological context influences pollinator deterrence by alkaloids in floral nectar

2007· article· en· W2094704842 on OpenAlexaff
Robert J. Gegear, Jessamyn S. Manson, James D. Thomson

Bibliographic record

VenueEcology Letters · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNectarPollinatorBiologyPollinationContext (archaeology)EcologyPollen

Abstract

fetched live from OpenAlex

Secondary compounds may benefit plants by deterring herbivores, but the presence of these defensive chemicals in floral nectar may also deter beneficial pollinators. This trade-off between sexual reproduction and defense has received minimal study. We determined whether the pollinator-deterring effects of a nectar alkaloid found in the perennial vine Gelsemium sempervirens depend on ecological context (i.e. the availability of alternative nectar sources) by monitoring the behavioural response of captive bumblebees (Bombus impatiens, an important pollinator of G. sempervirens in nature) to nectar alkaloids in several ecologically relevant scenarios. Although alkaloids in floral nectar tended to deter visitation by bumblebees, the magnitude of that effect depended greatly on the availability and nectar properties of alternative flowers. Ecological context should thus be considered when assessing ecological costs of plant defense in terms of pollination services. We consider adaptive strategies that would enable plants to minimize pollinator deterrence because of defensive compounds in flowers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.211
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations112
Published2007
Admission routes1
Has abstractyes

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