Probabilistic Approach for Design and Hydrologic Performance Assessment of Reconstructed Watersheds
Bibliographic record
Abstract
The oil sands mining industry in Canada has made a commitment to reclaim mining areas to an equivalent capability to that which existed prior to mining. An essential requirement in the design of reclamation covers to meet this objective is that all covers must have a sufficient available water holding capacity (AWHC) in order to supply sufficient moisture for vegetation over the summer moisture deficit typical in the region. AWHC is currently based on static evaluations of wilting point and field capacity under a constant annual evapotranspiration demand. This paper presents an alternative probabilistic approach by which the hydrologic performance of these reclamation soil covers can be assessed. A field-calibrated water balance model is used along with the available historical meteorological record to estimate the maximum soil moisture deficit that a soil cover is able to sustain over the growing season. Frequency curves of the maximum annual moisture deficit are used to assess the probability that a cover is able to provide any particular threshold of moisture demand. The method also allows for a quantification of the predictive uncertainty of the model. The predictive uncertainty is used as a margin of safety to estimate a design value of moisture deficit for various alternative cover designs. This paper recommends procedures for a frequency-based assessment and design of reclamation soil covers in the oil sands industry. This method takes into account climatic variability as well as parameter uncertainty in estimating the soil moisture deficit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".