Radiance fluctuations induced by surface waves can enhance the appearance of underwater objects
Bibliographic record
Abstract
To examine the effect of wave‐induced light fluctuations on the appearance of objects to fish, we recorded the spatial and temporal fluctuations of light reflected from a diffusely reflecting target that served as a simplified proxy for the body of a fish, and of light from the water background that a fish might be viewed against. Measurements were repeated at diverse depths, viewing azimuths, distances to the substrate, and sun conditions. Two conditions that are necessary for wave‐induced light fluctuations to make objects more apparent to fish were satisfied. The contrast of light fluctuations reflected from either the object or water background was higher than the minimum contrast value that is detected by fish, or, alternatively, the contrast of light fluctuations reflected from both the object and water background was higher than the minimum contrast value detected by fish, but differed from one another. Furthermore, the frequency range where most of the power of wave‐induced radiance fluctuations matched the frequency range of maximum contrast sensitivity in fish. Thus, light stimuli having spatial and temporal characteristics similar to those of wave‐induced light fluctuations may make objects more apparent to fish. We suggest that the frequency characteristics of the visual systems of fish were likely shaped by wave‐induced light fluctuations in aquatic ecosystems.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".