Camouflage behaviour and body orientation on backgrounds containing directional patterns
Bibliographic record
Abstract
The best-known interrelated mechanisms through which coloration can act to reduce predator detection rates of potential prey are background matching and disruptive coloration (Thayer 1909; Cott 1940; Kingsland 1978; Ruxton et al . 2004; Wilkinson & Sherratt 2008; Stevens & Merilaita 2009). With background matching, objects are difficult to detect simply due to their similarity to their background. Conversely, the striking/high-contrast markings involved in disruptive coloration create ‘the appearance of false edges and boundaries and hinders the detection or recognition of an object's outline and shape’ (Stevens & Merilaita 2009). Coloration is but one means through which animals achieve crypsis; others include behaviour and morphology, including body size and shape. Here we focus on behaviour and its interaction with coloration in relation to crypsis.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".