Novel molecular approach demonstrates that turbid river plumes reduce predation mortality on larval fish
Bibliographic record
Abstract
Turbidity associated with river plumes is known to affect the search ability of visual predators and thus can drive 'top-down' impacts on prey populations in complex ecosystems; however, traditional quantification of predator-prey relationships (i.e. stomach content analysis) often fails with larval fish due to rapid digestion rates. Herein, we use novel molecular genetic methods to quantify larval yellow perch (YP) in predator stomachs in western Lake Erie to test the hypothesis that turbidity drives variation in larval predation. We characterize predator stomach content DNA to first identify YP DNA (single nucleotide polymorphism) and then quantify larval YP predation (microsatellite allele counting) in two river plumes differing in turbidity. Our results showed elevated larval YP predation in the less turbid river plume, consistent with a top-down impact of turbidity on larval survival. Our analyses highlight novel ecological hypothesis testing using the power of innovative molecular genetic approaches.
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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) | 0.002 | 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".