Impact of turbidity on the gill morphology and hypoxia tolerance of eastern sand darter (<scp><i>Ammocrypta pellucida</i></scp>)
Bibliographic record
Abstract
Abstract Anthropogenic stressors such as agriculture and urbanization can increase river turbidity, which can negatively impact fish gill morphology and growth due to reduced oxygen in the benthic environment. We assessed the gill morphology, field metabolic rate (FMR), and two hypoxia tolerance metrics (oxygen partial pressure at loss of equilibrium, PO 2 at LOE, and critical oxygen tension, P crit ) of eastern sand darter ( Ammocrypta pellucida ), a small benthic fish listed as threatened under the Species at Risk Act in Canada, from rivers in southern Ontario. Field trials were conducted streamside in the Grand River (August 2019; mean NTU 8) and in the comparatively more turbid Thames River (August 2020; mean NTU 94) to test the effect of turbidity on each physiological endpoint. Gills were collected from incidental mortalities and museum specimens, and were assessed using hematoxylin and eosin and immunofluorescent staining. The between‐river comparison indicated that turbidity significantly increased interlamellar space and filament width but had no significant influence on other gill morphometrics or FMR. Turbidity significantly increased PO 2 at LOE (i.e., fish had a lower hypoxia tolerance) but did not significantly impact P crit . Therefore, although turbidity influences hypoxia tolerance through LOE, turbidity levels were not sufficiently high in the study rivers to contribute to measurable changes in gill morphology or metabolism in the wild. Determining whether changes in gill morphology or metabolism occur under higherturbidity levels would help resolve the ecological importance of turbidity on species physiology in urban and agricultural ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".