Animal perception in gravel-bed rivers: scales of sensing and environmental controls on sensory information
Bibliographic record
Abstract
Animals make decisions based on the sensory information that they obtain from the environment and other organisms within that environment. In a river, this information is transported, transmitted, masked, and filtered by fluvial factors and processes, such as relative roughness and turbulent flow. By interpreting the resultant signals, animals decide on the suitability of habitat and their reaction to other organisms. While a great deal is known about the sensory biology of animals, only limited attention has been paid to the environmental controls on the propagation of sensory information within rivers. Here, the potential transport mechanisms and masking processes of the sensory information used by animals in gravel-bed rivers are assessed by considering how the physical nature of sensory signals are affected by river hydromorphology. In addition, the physical processes that animals have the potential to directly perceive are discussed. Understanding the environmental phenomena that animals directly perceive will substantially improve understanding of what controls animal distributions, shifting emphasis from identifying correlations between biotic and abiotic factors to a better appreciation of causation, with benefits for successful management.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".