MétaCan
Menu
Back to cohort
Record W4404412539 · doi:10.1016/j.baae.2024.11.005

Positive association between foliar silicon and extrafloral nectar in Vicia faba with application of methyl jasmonate

2024· article· en· W4404412539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBasic and Applied Ecology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Foundation for Innovation
KeywordsVicia fabaMethyl jasmonateNectarBotanyBiologyPollen

Abstract

fetched live from OpenAlex

Plants have evolved direct and indirect defences against herbivores, which may come at a cost to other plant functions. Many plants can uptake and deposit large amounts of silicon within plant tissue, creating structures which can reduce herbivore performance. Silicon uptake can increase plant defenses against herbivores, but it has also been shown to trade-off with defensive phenolic compounds due to interference with jasmonic acid (JA) signaling. Additionally, plants can recruit predacious insects with extrafloral nectar (EFN), a sugar secretion not involved in pollination. It is currently unclear whether silicon uptake reduces other putative defences associated with the JA pathway, like EFN production. We used faba bean ( Vicia faba) to identify potential trade-offs between silicon accumulation, phenolic content, and EFN production. We grew four genotypes of faba bean that varied in tannin content in control soil, or soil supplemented with silicon. After five weeks of growth, we exposed plants to either a buffer or methyl jasmonate (MeJA) solution to simulate an herbivory response. We measured EFN production at 24 and 48 hours after treatment, and harvested leaves to quantify silicon and phenolic content. We found silicon supplementation, but not MeJA treatment, increased foliar silicon concentration. Silicon supplementation did not affect foliar phenolic content or EFN sugar content. Silicon concentration (ppm) and MeJA treatment did not decrease foliar phenolic content or EFN sugar content. However, we found an interaction between silicon concentration (ppm) and MeJA treatment with EFN sugar content: across MeJA-treated plants, we detected a positive association between foliar silicon concentration and the amount of sugar (mg) in EFN. This study is the first to show MeJA can interact with leaf silicon concentration to alter EFN response, with the potential for cascading effects on other trophic levels.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.186

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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