Animal Camouflage: Disentangling Disruptive Coloration from Background Matching
Bibliographic record
Abstract
Camouflage is ubiquitous in the natural world and provides adaptive benefits to both predators and their prey.In this study I test concepts of animal camouflage using the experimental paradigm of humans foraging for real and artificial moth targets on a computer screen and assessed camouflage efficacy by measuring detection rates.Chapter 1 outlines the questions and objectives of this doctoral thesis.In Chapter 2 I introduce the phenomenon of disruptive coloration, followed by a brief-review of the visual mechanisms contributing to visual search.Chapter 3 tested if non-random orientation behaviour of moths in the field could be explained by behaviourally-mediated camouflage.I showed that the preferred fieldorientations of moths were associated with lower detection rates in the lab, and that the relative orientation of the moth to the tree was the key driver.Chapter 4 tested the fundamental assumption that disruptive coloration functions by impairing shape perception.It was predicted that if edge-intersecting patches are disruptive, then altering the shape of a target would interact with edge coloration.Artificial moth-like targets did show an interaction between edge coloration and target shape, which explained detectability.These findings suggest that effectiveness of camouflage due to edge markings is dependent on target shape, which further supports the hypothesis that edge markings function as disruptive coloration.Chapter 5 took a similar approach to chapter 4 but tested if there was an interaction between edge coloration and target boundary visibility, which could explain detectability of moth-like targets.Results from Chapters 4 and 5 suggest that shape and boundary properties play a role in disruptive function of edge markings.Chapter 6 tested how this might occur.It is thought that edge-intersecting patches impair object recognition.It was predicted that moth-like targets with more edge-intersecting patches would be harder to recognise.Recognition was characterised by human foveal vision, monitored by eyetracking.Indeed, targets with a larger number of edge-intersecting patches were associated with being difficult to recognise, and reduced detectability even at the expense of background matching.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".