OVERVIEW OF BEST PRACTICES FOR SURFACE EROSION PROTECTION AND SEDIMENT CONTROL FOR THE DEVELOPMENT PHASE OF SURFACE MINING FOR COAL IN NORTHEAST BRITISH COLUMBIA
Bibliographic record
Abstract
Planning for surface erosion protection and sediment control for the development phase of recently developed surface coal mines in northeast BC has become an important part of the environmental management systems for these projects. Development of a surface mine involves significant land disturbance that typically results in increased rates of erosion and sedimentation. Planning to mitigate this impact must be proactive and integrated. The primary components of proactive planning for surface erosion protection and sediment control are completing assessments of soils and conducting overview hydrology assessments of potential surface and near-surface seepage areas. This information can be used to identify areas of higher risk for erosion and sediment generation and can be used to design a mine development plan that avoids or minimizes development of such areas if possible. Where development is unavoidable, this information can be used to design effective best practices for mitigating potential impacts resulting from a proposed mine development activity. Approaches for planning, developing, and implementing effective best practices for surface erosion protection and sediment control are presented in this paper. Examples of on the ground best practices are also provided.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".