Plant community, environment, and land-use data from oil sands reclamation and reference wetlands, Alberta, 2007–2009
Bibliographic record
Abstract
Our goal was to evaluate the success of wetland reclamation efforts on oil sands mining company lease-holdings in Alberta, Canada. Already, 60 200 ha of land have been disturbed by mining, and an additional 419 800 ha will be mined in the future. Wetland reclamation efforts have been underway for 35 years, and current mine closure plans call for the construction of 15 840 ha of wetland habitat. There are, however, no accepted criteria by which the Alberta Government can evaluate constructed wetlands. We employed the reference condition approach, comparing reclamation wetlands to appropriate natural analogues with plants as bioindicators of wetland condition. The data set includes 74 wetlands, spanning a range in salinity, nutrient levels, size, and degree of human disturbance. Reclamation wetlands include those contaminated with oil sands tailings (oil sands process affected, OSPA, n = 13) and those free from tailings (oil sands reference, OSREF, n = 12). In contrast, some natural wetlands are exposed to agricultural impacts (AG, n = 12), but the majority represent our least-disturbed condition (reference wetlands, REF, n = 37). The data set includes species relative abundance from plant communities in the wet meadow, emergent, and open-water vegetation zones. In addition, we measured water and sediment chemistry variables and physical variables to quantify local environmental conditions. We also quantified land use surrounding 45 of the wetlands in a series of nested buffers ranging from 300 m to 2000 m from the edge of each wetland's open-water zone. This data set represents the most comprehensive, publicly available data on reclamation wetlands from the lease areas of the two largest oil sands mining companies. It may be used to investigate changes in wetland plant community composition along both natural and human-caused environmental gradients, including contamination by oil sands mine tails. The data could also inform studies into the effects of surrounding land use on wetland plants and local-level environmental conditions. Information about reclamation wetlands could serve as a basis for tracking reclamation trajectories over time, whereas information about reference wetlands could be used to characterize natural variability in plant communities in shallow open-water wetlands from the Boreal Plains ecoregion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".