At‐A‐Station Hydraulic Geometry Simulator
Bibliographic record
Abstract
Abstract Presented in this paper is a hydraulic model that combines a rational regime theory with an at‐a‐station hydraulic geometry simulator (ASHGS) to predict reach‐averaged hydraulic conditions for flows up to but not exceeding the bankfull stage. The hydraulic conditions determined by ASHGS can be paired with an empirical joint frequency distribution equation and applicable habitat suitability indices to generate weighted usable area (WUA) as a function of flow. ASHGS was tested against a 2‐dimensional hydrodynamic model (River2D) of a mid‐size channel in the Interior Region of British Columbia. By linking ASHGS to a regime model, it becomes possible to evaluate the direction and magnitude of habitat changes associated with a wide range of environmental changes. Our regime model considers flow regime, sediment supply, and riparian vegetation: these governing variables can be used to simulate responses to forest fire, flow regulation and changes in climate and land use. Practitioners can examine ‘what‐if’ scenarios that otherwise would be too expensive and time consuming to fully explore. The model boundaries of commonly used data‐intensive hydraulic habitat models (e.g. PHABSIM) are not easily adjusted and such models are not designed to estimate future morphological and hydraulic habitat conditions in rivers the undergo significant channel restructuring. The proposed model has the potential to become an accepted flow assessment tool amongst practitioners due to modest data requirements, user‐friendliness, and large spatial applicability; it can be used to conduct preliminary assessments of channel altering projects and determine if in‐depth habitat assessments are justified.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".