Assessment of Success Biostabilization Techniques on Selected Watercrossings in Alberta
Bibliographic record
Abstract
Since the early 1980’s TransCanada PipeLines Ltd. (TransCanada) has employed a number of stream bank stabilization measures in an effort to minimize the loss of fish habitat and to reduce the risk of long term erosion and sedimentation. Traditional stream crossing stabilization involved the re-contouring of streambanks to a stable slope (generally 2:1) and then lining the banks with rock armour and seeding. TransCanada began using alternate techniques, primarily log-walls in 1981. Since 1981 TransCanada has evolved the use stream bank stabilization techniques to include bio-stabilization techniques such as live log-walls, fascines, live staking, and brush layering in combination with more traditional structures. In 2004, TransCanada initiated a two year project to assess the success of biostabilization techniques used on a number of watercrossing throughout the its’ Alberta System. A total of 22 stream crossings were assessed in 2004 and 24 in 2005. Sites assessed were located the foothills and boreal forest areas of Alberta. In the majority of cases the biostabilization methods utilized were still intact and functioning as planned by providing stable streambanks and fish habitat. Several factors appeared to be influencing the value of the measures employed. Cattle grazing on sites where fencing did not exclude cattle from the crossing site resulted in destruction or reduced value of the measures employed. Uncontrolled All Terrain Vehicle activity resulted in a reduced value of the measure employed. Implementation of biostabilization techniques in the winter months (frozen ground conditions) created challenges in implementation of biostabilization measures resulting in less favorable results compared to other sites constructed in early and late fall. Seeding to control surface erosion also appeared to affect overall success of woody vegetation used as part of the biostabilization techniques. Biostabilization techniques employed by TransCanad have been effective in stabilizing watercrossings and providing fish habitat. In designing biostabilization systems for watercrossings consideration should be given to overall stabilization objectives, stream flow information, fish and fish habitat values, and likelihood of success given the geographic region, timing of construction, and surrounding land-use pressures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".