Microdistribution of a torrential stream invertebrate: Are bottom‐up, top‐down, or hydrodynamic controls most important?
Bibliographic record
Abstract
Lay Abstract In general, stream food webs consist of algae (periphyton; primary producers growing on rocks) that are consumed by grazing invertebrates, which are in turn preyed upon by a variety of predators. Many invertebrate grazers avoid predators by hiding under rocks during the daytime when visual predators like fish are active, or by seeking high‐velocity microhabitats where invertebrate predators cannot access them. We examined the food web in a mountain stream in the Rocky Mountains by placing marked rocks in the streambed and measuring the distributions of local bed shear stress (force per unit area across the bottom; τw), periphyton, and herbivorous invertebrates. Grazing mayfly larvae (Epeorus longimanus (Eaton)) were the only invertebrates (grazer or predator) found in large numbers on the upper surface of stones. τw increased from the upstream to the downstream portion of stones, and large numbers of Epeorus larvae (up to 1500 larvae per square meter) migrated to these areas nightly. More periphyton was found on rougher and higher areas of the stones. Larval density was positively related to stone surface roughness and topography and to a lesser extent with periphyton and τw. Reversing the stones in the streambed revealed that Epeorus larvae responded to near‐bed flows, rather than to periphyton or predators. Hydrodynamics can have important effects on stream ecosystems and their food webs.
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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".