High complexity of aquatic irradiance may have driven the evolution of four-dimensional colour vision in shallow-water fish
Bibliographic record
Abstract
Humans use three cone photoreceptor classes for colour vision, yet many birds, reptiles and shallow-water fish are tetrachromatic and use four cone classes. Screening pigments, which narrow the spectrum of photoreceptors in birds and diurnal reptiles, render visual systems with four cone classes more efficient. To date, however, the question of tetrachromacy in shallow-water fish that, like humans, lack screening pigments, is still unsolved. We raise the possibility that tetrachromacy in fish has evolved in response to higher spectral complexity of underwater light. We compared the dimensionality of colour vision in humans and fish by examining the spectral complexity of the colour signal reflected from objects into their eyes. We show that fish require four to six cone classes to reconstruct the colour signal of aquatic objects at the accuracy level achieved by humans viewing terrestrial objects. This is because environmental light, which alters the colour signals, is more complex and contains more spectral fluctuations underwater than on land. We further show that fish cones are better suited than human cones to detect these spectral fluctuations, suggesting that the capability of fish cones to detect high-frequency fluctuations in the colour signal confers an advantage. Taken together, we propose that tetrachromacy in fish has evolved to enhance the reconstruction of complex colour signals in shallow aquatic environments. Of course, shallow-water fish might possess fewer than four cone classes; however, this would come with the inevitable loss in accuracy of signal reconstruction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".