The retinal topography of the red lionfish (Pterois volitans): towards an understanding of the visual system of a highly invasive species
Bibliographic record
Abstract
Retinal specializations provide organisms with the spatial information needed to engage in visually guided behaviours aimed at maximizing fitness; nevertheless, little is known about the retinal topography of crepuscular fish, as most studies address diurnal or deep-sea species. I evaluated the types of photoreceptor and neural cells in the retina of the red lionfish, their distribution in the retinal surface, and their spatial resolving power. Single, double and triple cones are imbedded in a rod-dominated field over the entire inner layer. Single and double cones occur in higher densities towards the dorsal and ventral areas, arranged in a square mosaic pattern all along except in the central region of the retina, where they concur with triple cones. Ganglion and amacrine cells cover the outer layer with higher densities in the ventral-nasal region, a mismatch to single and double cones, and occasionally assemble as glomeruli or striation. Cell distribution and single and double cone arrangements in conjunction with spatial resolving power values suggest vision in P. volitans is adapted for vertically open habitats and performs better in dim-light environments while maintaining a moderate capacity for color vision, which denotes an advantage for predatory capabilities and success as an invasive species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".