Comparative probabilistic assessment of the hydrological performance of reconstructed and natural watersheds
Bibliographic record
Abstract
Abstract The oil sands industry has committed to returning the mine sites to a productive condition. The reconstructed soil covers must have sufficient available water holding capacity (AWHC) to supply enough moisture over the growing season, to promote vegetation. In order to assess the sustainability of various soil cover alternatives, a generic, system dynamic watershed model entitled GSDW was used along with the available historical meteorological records to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted; consequently, frequency curves of the maximum annual moisture deficit values are constructed and used to assess the probability that various reconstructed and natural watersheds can provide the associated moisture demands. In general, the study showed a tendency for the reconstructed watershed to provide less moisture for evapotranspiration than natural systems. Watersheds of various soil types, layering, thicknesses and topography were studied. The gained knowledge was used to predict the possible performance of a hypothetical reclamation cover. The results indicated that the hypothetical cover performed in a similar manner to the thickest existing soil cover which confirmed a high probability of that cover to survive under the same existing climatic conditions. Moreover, this probabilistic framework was found to be useful for integrating information gained from natural watersheds (e.g. the canopy of mature natural systems and transfer the results to the reconstructed system). The results show that the canopy influenced the moisture deficit regime positively which signifies a greater possibility that reconstructed covers will adapt to vegetation type. In brief, the adopted approach enables better understanding of the response of reconstructed systems via multiple simulations of ‘what‐if’ scenarios using different soil/vegetation alternatives. Copyright © 2010 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".