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Gregariousness and repellent defences in the survival of phytophagous insects

2000· article· en· W1995433588 on OpenAlexfundno aff
Alison F. Hunter

Bibliographic record

VenueOikos · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsDalhousie University
KeywordsPredationBiologyPredatorLarvaLepidoptera genitaliaEcologyInstarZoology

Abstract

fetched live from OpenAlex

Group living has both costs and benefits for plant‐feeding insects, but defence against predators is the most widely acknowledged benefit. Gregarious folivores typically have warning coloration and elaborate anti‐predator defences. Do these defences protect these species from predation? To see if protection from predators generally results from gregariousness, I compared the shapes of published survivorship curves of externally feeding, gregarious and solitary Lepidoptera and Symphyta. Gregarious species are less likely than solitary species to die in the larval stages. However, solitary species that have anti‐predator defences do not have higher larval survival compared to gregarious species. This result, along with evidence from experimental manipulations of group size, suggests that repellent defences per se do not increase survival of gregarious larvae. Group behaviour is undoubtedly important in affecting the higher larval survival of gregarious species, but we currently cannot determine whether predator learning, dilution of risk, or rapid development contribute most to increasing survival.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.035
GPT teacher head0.206
Teacher spread0.171 · 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

Citations145
Published2000
Admission routes1
Has abstractyes

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