Bibliographic record
Abstract
Abstract The book discusses the diversity of mechanisms by which prey can avoid or survive attacks by predators, both from ecological and evolutionary perspectives. There is a particular focus on sensory mechanisms by which prey can avoid being detected, avoid being identified, signal (perhaps sometimes dishonestly) to predators that they are defended or unpalatable. The book is divided into three sections. The first considers detection avoidance through, for example, background matching, disruptive patterning, countershading and counterillumination, or transparency and reflective silvering. The second section considers avoiding or surviving an attack if detection and identification by the predator has already taken place (i.e., secondary defences). The key mechanism of this section is aposematism: signals that warn the predator that a particular prey type is defended. One particularly interesting aspect of this is the sharing of the same signal by more than one defended species (the phenomenon of Mullerian mimicry). The final section considers deception of predators. This may involve an undefended prey mimicking a defended species (Batesian mimicry), or signals that deflect predator’s attention or signals that startle predators. The book provides the first comprehensive survey of adaptive coloration in a predator-prey context in thirty years.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.156 | 0.081 |
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".