Bibliographic record
Abstract
Abstract Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of concealment strategies, disruptive coloration is thought to visually fragment an animal’s’ outline, thereby reducing its rate of discovery. Here, I propose two non-mutually exclusive hypotheses for how disruptive camouflage functions, and describe the visual mechanisms that might underlie them. (1) The local edge disruption hypothesis states that camouflage is achieved by breaking up edge information. (2) The global feature disruption hypothesis states camouflage is achieved by breaking up the characteristic features of an animal (e.g., overall shape or facial features). Research clearly shows that putatively disruptive edge markings do increase concealment; however, few tests have been undertaken to determine whether this survival advantage is attributable to the distortion of features, so the global feature disruption hypothesis is under studied. In this review the evidence for global feature disruption is evaluated. Further, I address if object recognition processing provides a feasible mechanism for animals’ features to influence concealment. This review concludes that additional studies are needed to test if disruptive camouflage operates through the global feature disruption and proposes future research directions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".