Evaluating spatially explicit metrics of stream energy gradients using hydrodynamic model simulations
Bibliographic record
Abstract
Localized energy gradients and velocity shelters created by boulders, bars, and channel banks are often essential components of aquatic habitat. Two-dimensional hydraulic models have the potential to predict the amounts and locations of such spatially varying flow patterns. However, little effort has been devoted to reproducing these flow features and developing spatial habitat metrics to describe and differentiate between various types of flow patterns. Two-dimensional numerical simulations, based on actual channel geometry, are used here to model a variety of flow patterns encountered in natural streams. The simulation results are used to develop spatial habitat metrics that quantify local velocity gradients and changes in kinetic energy. The proposed metrics are evaluated at various points within the different flow patterns of interest. The metrics produce large values for flow patterns exhibiting considerable spatial variation and small values in areas experiencing uniform flow conditions. Comparisons with other researchers' field data suggest that the metric values produced in the modeled flows are consistent with values found near fish resting and feeding locations. The habitat metrics, measures of the flow's rate of spatial change in kinetic energy, can also be incorporated into bioenergetic models to facilitate the computation of fish energy expenditure rates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".