Assessing the potential of integrating distribution and structure of permanent open-water wetlandscapes in reclamation design: a case study of Alberta, Canada
Bibliographic record
Abstract
Abstract Wetlands are multi-functional systems that provide a disproportionate number of ecosystem services given the spatial extent they occupy both nationally and globally. The ecological functioning of these wetlands is dependent on the structure of the landscape, which poses unique challenges when reclaiming wetlands in areas where resource extraction is occurring. Resource extraction mega-projects require that entire wetlandscapes be reclaimed and often involve timelines that necessitate the consideration of climate projections to create self-sustaining, naturally appearing wetlandscapes that meet policy objectives. To understand wetlandscape structure and guide reclamation planning and closure permitting evaluation, a random sample of 13,676 1-km2 landscapes were subselected to identify 1684 permanent open-water wetlandscapes. A parsimonious set of landscape metrics were applied and compared across levels of anthropogenic disturbance and across natural regions (i.e., Grassland, Parkland and Boreal). Results demonstrated that permanent open-water wetlands are relatively rare (12.3% of our total random sample) and typically occupy less than 8% of wetlandscapes when present. The majority of wetlands in the study area are less permanent and more variable in nature than the permanent open water wetlandscapes created by megaproject reclamation, which has the potential to alter the distribution and size of open-water wetlands beyond their natural occurrence. Comparison across disturbance levels and natural regions yield statistical differences among landscape structure. General wetland landscapes representing a combination of disturbance level and natural region can be created for each metric to guide reclamation design and closure planning approval.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".