Identifying links between Fluvial Geomorphic Response Units (FGRUs) and fish species in the Assiniboine River, Manitoba
Bibliographic record
Abstract
Abstract The Assiniboine River, located in east central Saskatchewan and southwestern Manitoba, provides recreational opportunities, irrigation, and industrial and municipal water resources to Manitoba residents while supporting a diverse fish fauna. Improving our understanding of patterns in the spatial distribution of different fish species in relation to physical habitat features can aid management of this important water resource. The Fluvial Geomorphic Response Unit (FGRU) method is a geospatial modelling technique that allows the classification of large‐scale river reaches that exhibit similar geomorphic structure and provide a link between the hydrological regime and physical riverine habitats. Historical electrofishing data provide catch per unit effort data for various fish species and allow an investigation of fish distribution among different FGRUs. This study has identified significant differences (Kruskal–Wallis test,p < 0·05) in the catch per unit effort between three FGRUs for ten fish species in the Assiniboine River. These findings have the potential to increase our understanding of habitat complexity, availability, and connectivity in prairie rivers, a valuable tool for resource managers. This model can contribute to the development of sampling programmes by allowing efficienta priorisite selection, ensuring sampling of diverse unit types and thus a greater representation of the range of physical habitats present within a river system. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".