Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In animals, iridescence is generated by the interaction of light with biological tissues that are nanostructured to produce thin films or diffraction gratings. Uniquely among animal visual signals, the study of iridescent coloration contributes to biological and physical sciences by enhancing our understanding of the evolution of communication strategies, and by providing insights into physical optics and inspiring biomimetic technologies useful to humans. Iridescent colours are found in a broad diversity of animal taxa ranging from diminutive marine copepods to terrestrial insects and birds. Iridescent coloration has received a surge of research interest of late, and studies have focused on both characterizing the nanostructures responsible for producing iridescence and identifying the behavioural functions of iridescent colours. In this paper, we begin with a brief description of colour production mechanisms in animals and provide a general overview of the taxonomic distribution of iridescent colours. We then highlight unique properties of iridescent signals and review the proposed functions of iridescent coloration, focusing, in particular, on the ways in which iridescent colours allow animals to communicate with conspecifics and avoid predators. We conclude with a brief overview of non-communicative functions of iridescence in animals. Despite the vast amount of recent work on animal iridescence, our review reveals that many proposed functions of iridescent coloration remain virtually unexplored, and this area is clearly ripe for future research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it