Opportunistic wetland formation on reconstructed landforms in a sub-humid climate: influence of site and landscape-scale factors
Bibliographic record
Abstract
Abstract Initiation of wetland features is integral to sustaining landscape eco-hydrological function and meeting defined goals in surface mine reclamation. Within the sub humid climate of the Athabasca Oil Sands, Canada, the water generation mechanisms (external water sources, internal feedback mechanisms) that enable wetlands to form opportunistically on recently reconstructed landscapes are currently unknown, restricting the flexibility in mine closure planning. To address this knowledge gap, we interpret site and local physical characteristics of opportunistic wetlands within the Athabasca Oil Sands through a synoptic survey. Wetlands formed in ~ 8% of the random survey transect areas designed and planted for forestlands. Wetlands had vegetation structures characteristic of woody Salix spp. swamps and narrow-leaved Carex spp. marsh wetland types, with minor coverage of open water marshes. Wetlands formed opportunistically over a range of slopes, aspects and topographic positions, across contrasting fine and coarse-textured landforms. However, different wetland establishment and maintenance controls exist on fine and coarse-textured landforms. On coarse-textured landforms with large groundwater transmissivity, wetland formation was influenced by landscape-scale factors; wetlands were restricted to the toes of slopes and areas intersecting groundwater. On fine-textured constructed landforms, small and large wetlands occurred on lower landscape elevations with the potential for the external (cumulative) water sources, and in hydrologically isolated locations with little potential for runoff contribution from adjacent forestlands (saturation and wetland formation through internal feedback mechanisms). Regardless of landscape position, wetlands formed on flat areas and in shallow inward draining endorheic pans with clay rich soils where low water storage potential promotes frequent surface saturation. These findings have important implications in landscape reclamation design, suggesting that passive techniques that support internal feedback mechanisms may offer a more cost effective reclamation approach compared to more active, expensive techniques that aim to develop wetlands with external water sources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".