Gill slits provide a window into the respiratory physiology of sharks
Bibliographic record
Abstract
Metabolically important traits, such as gill surface area and metabolic rate, underpin life histories, population dynamics and extinction risk, as they govern the availability of energy for growth, survival and reproduction. Estimating both gill surface area and metabolic rate can be challenging, especially when working with large-bodied, threatened species. Ideally, these traits, and respiratory physiology in general, could be inferred from external morphology using a faster, non-lethal method. Gill slit height is quick to measure on live organisms and is anatomically connected to the gill arch. Here, we relate gill slit height and gill surface area for five Carcharhiniform sharks. We compared both total and parabranchial gill surface area to mean and individual gill slit height in physical specimens. We also compared empirical measurements of relative gill slit height (i.e. in proportion to total length) to those estimated from field guide illustrations to examine the potential of using anatomical drawings to measure gill slit height. We find strong positive relationships between gill slit height and gill surface area at two scales: (i) for total gill surface area and mean gill slit height across species and (ii) for parabranchial gill surface area and individual gill slit height within and across species. We also find that gill slit height is a consistent proportion of the fork length of physical specimens. Consequently, relative gill slit height measured from field guide illustrations proved to be surprisingly comparable to those measured from physical specimens. While the generality of our findings needs to be evaluated across a wider range of taxonomy and ecological lifestyles, they offer the opportunity that we might only need to go to the library and measure field guide illustrations to yield a non-lethal, first-order approximation of the respiratory physiology of sharks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".