Bibliographic record
Abstract
Mimicry often involves a protective element, whereby the risk of predation on mimics is reduced owing to their resemblance to unpalatable models. However, protection from predation has so far seemed unimportant in aggressive mimicry, where mimics are usually predators rather than prey. Here, we demonstrate that bluestriped fangblennies (Plagiotremus rhinorhynchos), which are aggressive mimics of juvenile bluestreak cleaner wrasse (Labroides dimidiatus), derive significant protection benefits from their resemblance to cleaner fish. Field observations revealed that mimetic fangblennies were chased by potential victims less often than individuals of a closely related, ecologically and behaviourally similar but non-mimetic species (Plagiotremus tapeinosoma). After attacks, proximity to models protected mimics from retaliation by victims, but the effect of colour similarity was less clear. Both colour resemblance and physical proximity to models thus appear to protect cleaner-fish mimics from aggression by potential and actual victims of their attacks. Our results suggest that the mimicry types observed in nature, which are usually distinguished on the basis of the benefits accrued to mimics, may in fact overlap greatly in the benefits provided.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".