Microdistribution of a torrential stream invertebrate: Are bottom‐up, top‐down, or hydrodynamic controls most important?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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; τ w ), 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. τ w 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 τ w . 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it