STREAM RESTORATION AND CRIBWALL PERFORMANCE: A CASE STUDY OF CRIBWALL MONITORING IN SOUTHERN ONTARIO
Bibliographic record
Abstract
ABSTRACT Stream restoration focusing on adaptable natural and inert material use has been implemented through soil bioengineering designs aimed at the stabilization of urbanized streams. Within each design application materials such as large wood, sediment fill and vegetation must be suited to diverse settings. This paper discusses the application of cribwalls as soil bioengineering designs found in two Southern Ontario watersheds and the criteria that influence their performance. Field measurements of cribwall cuttings, sediment sampling, erosion pin monitoring, and computer‐generated stream power analysis are used to compare design performance at several sites. It is determined that the technical specifications of the design and site characteristics such as stream power distribution, sediment, and channel planform are equally involved in long‐term streambank stability. The results indicate that cribwalls with dense cutting growth perform well on streambanks that offer a greater amount of soil cohesion, nutrients, and infiltration in the mid and upper sections of the bank. In streams with moderate channel slopes and stream power distribution that is above the watershed mean, streams with well‐developed floodplains, sinuous channel planforms, and low bank height ratios perform better than those that are confined, straightened, and have greater bank height ratios. Throughout the comparison of several cribwall sites, the implication of this work is to demonstrate how to assess the fitness of similar soil bioengineering designs for application to diverse stream settings and to further validate their significance in stream restoration as designs that are multifunctional. Copyright © 2013 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".