Hydraulic geometry as a physical template for the River Continuum: application to optimal flows and longitudinal trends in salmonid habitat
Bibliographic record
Abstract
The River Continuum Concept lacks a quantitative physical model to represent downstream trends in habitat. We evaluate whether hydraulic geometry relationships can be used as a physical template to predict longitudinal trends in habitat availability and optimal flows for different life-history stages of rainbow trout (Oncorhynchus mykiss). Optimal flows based on hydraulic geometry indicate that (i) optimal flows are higher for larger fish, (ii) optimal flows proportionally increase as streams became smaller and decrease downstream, and (iii) maximum predicted habitat suitabilities for fry and juveniles are in small streams, and maximum suitabilities are displaced progressively downstream for later life-history stages. These patterns are an emergent property of downstream increases in channel depth and velocity and changes in habitat suitability curves associated with increased swimming performance of larger fish. Nonlinear downstream trends in habitat imply that fixed flow percentages recommended by the Tenant method may substantially underestimate optimal flows in small streams. Despite oversimplifying complex channel structure, hydraulic geometry relationships should serve as a useful physical model for testing downstream trends in habitat-related processes along the river continuum.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".