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Record W2126091912 · doi:10.1111/ele.12205

Volatile communication between plants that affects herbivory: a meta‐analysis

2013· review· en· W2126091912 on OpenAlexaff
Richard Karban, Louie H. Yang, Kyle F. Edwards

Bibliographic record

VenueEcology Letters · 2013
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsHerbivoreBiologyResistance (ecology)EcologyHabitat

Abstract

fetched live from OpenAlex

Volatile communication between plants causing enhanced defence has been controversial. Early studies were not replicated, and influential reviews questioned the validity of the phenomenon. We collected 48 well-replicated studies and found overall support for the hypothesis that resistance increased for individuals with damaged neighbours. Laboratory or greenhouse studies and those conducted on agricultural crops showed stronger induced resistance than field studies on undomesticated species, presumably because other variation had been reduced. A cumulative analysis revealed that early, non-replicated studies were more variable and showed less evidence for communication. Effects of habitat and plant growth form were undetectable. In most cases, the mechanisms of resistance and alternative hypotheses were not considered. There was no indication that some response variables were more likely to produce large effects. These results indicate that plants of diverse taxonomic affinities and ecological conditions become more resistant to herbivores when exposed to volatiles from damaged neighbours.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.224
GPT teacher head0.291
Teacher spread0.067 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations299
Published2013
Admission routes1
Has abstractyes

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