Riparian classification to benchmark reclamation of the Athabasca oil sands
Bibliographic record
Abstract
A Riparian Classification and Reclamation Guide (‘Riparian Guide’) was recently produced to direct the re-establishment of riparian ecosystems in areas disturbed by oil sands mining in Alberta, Canada. The Riparian Guide presents information on the identification, characterisation, and implementation of revegetation and monitoring protocols on reclaimed landscapes within the oil sands region. The purpose of the guide was to provide a classification system and tools associated with plant species and community interactions that can be used to effectively reclaim and revegetate disturbed riparian ecosystems. The Riparian Guide thus provides a framework for interpreting the environmental conditions of sites to be reclaimed, plant species requirements, landscape design and management objectives to facilitate reclamation activities and aid in the re-establishment of plant communities that were representative of riparian vegetation. An overview of the process that led to the development of the Riparian Guide is presented, including monitoring and riparian classification system that formed the foundation of the guide, a species selection tool, and a peer review. Finally, the riparian species selection tool is tested in a field program and the preliminary results of this test are reviewed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".