The effect of turbidity on recognition and generalization of predators and non‐predators in aquatic ecosystems
Bibliographic record
Abstract
Recent anthropogenic activities have caused a considerable change in the turbidity of freshwater and marine ecosystems. Concomitant with such perturbations are changes in community composition. Understanding the mechanisms through which species interactions are influenced by anthropogenic change has come to the forefront of many ecological disciplines. Here, we examine how a change in the availability of visual information influences the behavior of prey fish exposed to potential predators and non-predators. When fathead minnows, Pimephales promelas, were conditioned to recognize predators and non-predators in clear water, they showed a highly sophisticated ability to distinguish predators from non-predators. However, when learning occurred under conditions of increased turbidity, the ability of the prey to learn and generalize recognition of predators and non-predators was severely impaired. Our work highlights that changes at the community level associated with anthropogenic perturbations may be mediated through altered trophic interactions, and highlights the need to closely examine behavioral interactions to understand how species interactions change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".