Vision in elasmobranchs and their relatives: 21st century advances
Bibliographic record
Abstract
This review identifies a number of exciting new developments in the understanding of vision in cartilaginous fishes that have been made since the turn of the century. These include the results of studies on various aspects of the visual system including eye size, visual fields, eye design and the optical system, retinal topography and spatial resolving power, visual pigments, spectral sensitivity and the potential for colour vision. A number of these studies have covered a broad range of species, thereby providing valuable information on how the visual systems of these fishes are adapted to different environmental conditions. For example, oceanic and deep-sea sharks have the largest eyes amongst elasmobranchs and presumably rely more heavily on vision than coastal and benthic species, while interspecific variation in the ratio of rod and cone photoreceptors, the topographic distribution of the photoreceptors and retinal ganglion cells in the retina and the spatial resolving power of the eye all appear to be closely related to differences in habitat and lifestyle. Multiple, spectrally distinct cone photoreceptor visual pigments have been found in some batoid species, raising the possibility that at least some elasmobranchs are capable of seeing colour, and there is some evidence that multiple cone visual pigments may also be present in holocephalans. In contrast, sharks appear to have only one cone visual pigment. There is evidence that ontogenetic changes in the visual system, such as changes in the spectral transmission properties of the lens, lens shape, focal ratio, visual pigments and spatial resolving power, allow elasmobranchs to adapt to environmental changes imposed by habitat shifts and niche expansion. There are, however, many aspects of vision in these fishes that are not well understood, particularly in the holocephalans. Therefore, this review also serves to highlight and stimulate new research in areas that still require significant attention.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".