Case Studies of Instream Flow Modelling for Fish Habitat in Canadian Prairie Rivers
Bibliographic record
Abstract
Fish habitats represent some of the most difficult ecological, biological, topographic and hydrodynamic phenomena to evaluate and simulate in detail. This reality has led to the development of diverse approaches and a variety of methodologies in an attempt to provide a scientific basis for river management decisions both for ice-free and ice-covered seasons. Hydrodynamic and computational advances, and particularly the introduction of 2-dimensional numerical models, have provided better tools and renewed impetus for tackling some of the complexities of habitat simulation and habitat changes with river discharge. The 2-dimensional, depth-averaged, finite element hydrodynamic model RIVER2D has been utilized for several study sites in Canada and the USA. The latest version of this model, with improved interface and documentation, as well as an ice-cover module is available at www.river2d.ca. The model was used for instream flow analyses at several sites on Canadian prairie rivers, including sections of the Kananaskis, the North and South Saskatchewan and the Assiniboine. The hydrodynamic results, coupled with biologically-significant suitability metrics, were used to develop relationships between weighted useable fish habitat areas and river discharge. As in most instream flow case studies, such analyses are supplemented by site-specific investigations and professional judgement, which can be guided by mimicking natural systems (physiomimesis). Preserving key components of natural hydrographs may have a higher chance of maintaining ecological function and is amenable to adaptive management frameworks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".