Why different regions of the retina have different spectral sensitivities: A review of mechanisms and functional significance of intraretinal variability in spectral sensitivity in vertebrates
Bibliographic record
Abstract
Vision is used in nearly all aspects of animal behavior, from prey and predator detection to mate selection and parental care. However, the light environment typically is not uniform in every direction, and visual tasks may be specific to particular parts of an animal's field of view. These spatial differences may explain the presence of several adaptations in the eyes of vertebrates that alter spectral sensitivity of the eye in different directions. Mechanisms that alter spectral sensitivity across the retina include (but are not limited to) variations in: corneal filters, oil droplets, macula lutea, tapeta, chromophore ratios, photoreceptor classes, and opsin expression. The resultant variations in spectral sensitivity across the retina are referred to as intraretinal variability in spectral sensitivity (IVSS). At first considered an obscure and rare phenomenon, it is becoming clear that IVSS is widespread among all vertebrates, and examples have been found from every major group. This review will describe the mechanisms mediating differences in spectral sensitivity, which are in general well understood, as well as explore the functional significance of intraretinal variability, which for the most part is unclear at best.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".