ENHANCED GEOMORPHIC DESIGN FOR RECLAMATION OF RURAL WASTE-SCAPES
Bibliographic record
Abstract
Many inventive concepts for the adaptive re-use of waste landscapes, or waste-scapes, have been proposed and constructed in the last decade. These are often located near or within large, urban populations, which provide much of the incentive for adaptive re-use. A different challenge presents itself when a waste-scape is rurally located, near a small - though equally important - population. How do we address complex socio-cultural, economic, and environmental objectives without the economic incentive provided by a large nearby population? This project looks at the mineable oil sands region of northern Alberta, Canada: a rural waste-scape covering 895 km2 in Canada’s boreal forest. Specifically, this project discusses the geomorphology and native substrate of northern Alberta, juxtaposed with the traditional design of waste storage landforms, in order to show that there are no natural analogues in the region. A geomorphic approach to the design of waste-scapes in this region has been developed using a Landscape Evolution Model (LEM) for long-term projections, and is being tested in the region. This project sheds new light on the rarely acknowledged issue of waste design in rural areas and the wide range of benefits achieved through use of an enhanced geomorphic design approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".