1Centre for Advanced Numerical Simulation (CANSIM)
Bibliographic record
Abstract
An example of watershed disturbance activity undertaken to gain access to the oil sands is large scale mining in the Athabasca basin, Alberta, Canada. One of the remedial activities of this disturbance is the reclamation of the disturbed lands. In the process of reclamation, the overburden soil is placed back into the mined pits and reformed with soil covers (alternatively called reconstructed watersheds). In the design process of reclamation, a major concern is hydrological sustainability, which includes the soil’s ability to store enough moisture for the water requirements of vegetation growth and land-atmospheric moisture fluxes. Typically, the goal of the reclamation is to restore the disturbed watersheds, so that they mimic the natural watersheds in terms of the ecological sustainability. Therefore, a comparative evaluation of the hydrological sustainability of the reconstructed watersheds with natural watersheds is required. The considered reconstructed watershed in this study (the flat top of the South Bison Hill, Fort McMurray, Alberta, which is about 6 years old) constitutes a thin layer of a peat-mineral mix (20 cm thick) overlying an 80 cm thick secondary (glacial till) layer on the shale formation, mimicking the natural soil horizons of undisturbed watersheds. As the reconstructed watershed is located in the boreal forest region, a mature boreal forest (Old Aspen site, about 88 years old) located in the Southern Study Area (SSA),
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.114 | 0.018 |
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".