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Record W3084319996 · doi:10.1098/rspb.2020.1894

Prey with hidden colour defences benefit from their similarity to aposematic signals

2020· article· en· W3084319996 on OpenAlexaff
Yongsu Kim, Yerin Hwang, Sangryong Bae, Thomas N. Sherratt, Jeong‐Seop An, Sei‐Woong Choi, Jeffrey C. Miller, Changku Kang

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsCarleton University
FundersNational Research Foundation of Korea
KeywordsAposematismPredationBiologyPredatorSimilarity (geometry)Predator avoidanceFlash (photography)CommunicationEcologyArtificial intelligenceComputer sciencePsychology

Abstract

fetched live from OpenAlex

Some camouflaged animals hide colour signals and display them only transiently. These hidden colour signals are often conspicuous and are used as a secondary defence to warn or startle predators (deimatic displays) and/or to confuse them (flash displays). The hidden signals used in these displays frequently resemble typical aposematic signals, so it is possible that prey with hidden signals have evolved to employ colour patterns of a form that predators have previously learned to associate with unprofitability. Here, we tested this hypothesis by conducting two experiments that examined the effect of predator avoidance learning on the efficacy of deimatic and flash displays. We found that the survival benefits of both deimatic and flash displays were substantially higher against predators that had previously learned to associate the hidden colours with unprofitability than against naive predators. These findings help explain the phenological patterns we found in 1568 macro-lepidopteran species on three continents: species with hidden signals tend to occur later in the season than species without hidden signals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.212
Teacher spread0.137 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Royal Society B Biological Sciences→Same topicPlant and animal studies→French-language works237,207→