Assessing the impact of riprap bank stabilization on fish habitat: \nA study of Lowland and Appalachian streams in Southern Québec
Bibliographic record
Abstract
There is a growing concern over the potential environmental impacts of riverbank stabilization using rock riprap as the occurrence of these structures continues to increase in river networks. Habitat diversity and quality are often used as a proxy for fish community health. Habitat assessments, however, frequently yield contrasting results between studies and it remains unclear how non-salmonid species in small streams may be affected by bank stabilization. The aim of this thesis was to evaluate how riprap structures impact fish habitat in small Lowland and Appalachian streams by combining quantitative and qualitative approaches. Metrics measured were: mesohabitat and in-stream cover proportions, Hydro-Morphological Index of Diversity (HMID), and a modified Qualitative Habitat Evaluation Index (QHEI). Results show that in more pristine Appalachian streams, QHEI scores are lower at stabilized reaches due to loss of in-stream cover and riparian vegetation. However, riprap stabilization had less impact on already altered, straightened Lowland streams. In this latter context, some possibly beneficial alterations of fish habitat were observed in riprapped reaches due to the coarsening of the substrate and an induced increase of slope. These positive effects are, however, limited to short stabilized reaches, and extensive (> 100 m) riprapping of the bed should be avoided as it can result in the drying of the bed during summer months, as was observed in this study in some tributaries of the Salvail River. Both metrics (HMID and QHEI) revealed the positive or neutral effect of riprap on increasing flow diversity and heterogeneity for Lowlands sites with a correlation of 0.72 (p <0.01). However their effect scores are inconsistent in the Appalachian streams as only QHEI showed a negative effect of riprap, suggesting caution when interpreting habitat quality results based on a single metric.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".