Wetlands happen: the delineation and classification of opportunistic wetlands in the Athabasca oil sands region of Canada
Bibliographic record
Abstract
Achieving land capability equivalent to that which existed prior to disturbance is the primary goal of reclamation in the Athabasca Oil Sands Region of northern Alberta. To date, most reclamation has focused on the re-creation of upland forest ecosystem analogues. However, a few wetlands have also been constructed. Additionally, wetlands have appeared spontaneously on landforms reclaimed to an upland forest type. Classifying and quantifying these opportunistic wetlands is an important consideration relative to oil sands closure and reclamation planning. Here we describe an approach using topographic and spectral variables to train a machine learning model (random forest) to detect and classify wetlands as an alternative to on-screen visual delineation. The aim was to develop a model that not only predicts where wetlands occur on reclaimed landforms but that is sensitive enough to classify them as to wetland form. Two random forest models were developed that predicted wetland occurrence at two levels: (1) wetland vs. non-wetland (to generate a prediction of all wet areas on reclaimed landforms); and (2) wetland class (with specific emphasis on marsh and shallow open water wetland classes). In addition to successfully predicting wetland occurrence, the resulting models handled the variability in reclamation approach, substrate type, and soil placement depth with high accuracy. This work confirmed that ~ 18% (211 ha) of the upland-reclaimed area at Suncor Energy’s Base Plant north of Fort McMurray, Alberta develops not to upland but to unintentional wetland, consistent with earlier studies. The ability to predict wetlands on the landscape could be invaluable when considering metrics of success associated with landscape reclamation in the Athabasca Oil Sands Region and for informing future inquiries around wetland persistence, resilience, and spatial connectivity through time on reclaimed landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".