Animal camouflage: biology meets psychology, computer science and art
Bibliographic record
Abstract
Animal camoufl age provides some of the most striking examples of the workings of natural selection, whether employed defensively to reduce predation risk, or offensively to minimise alerting prey.While the general benefi ts of camoufl age are obvious, understanding the precise means by which the viewer is fooled represent a challenge to a biologist, because camoufl age is an adaptation to the eyes and mind of another animal.Therefore, a full understanding of the mechanisms of camoufl age requires an interdisciplinary investigation of the perception and cognition of non-human species, involving the collaboration of biologists, neuroscientists, perceptual psychologists and computer scientists.Modern computational neuroscience grounds the principles of Gestalt psychology, and the intuition of generations of artists, in specifi c mechanisms that can be tested.We review the various forms of animal camoufl age from this perspective, illustrated by the recent upsurge of experimental studies of long-held, but largely untested, theories of defensive colouration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".