Climate, land cover and topography: essential ingredients in predicting wetland permanence
Bibliographic record
Abstract
Wetlands in the Prairie Pothole Region (PPR) are forecast to retract in their ranges due to climate change, and potholes that typically contain ponded water year-round, which support a larger proportion of biological communities, are most sensitive to climate change. In addition to climate, land use activities and topography also influence ponded water amounts in PPR wetlands. However, topography is not typically included in models forecasting the impacts of climate change on PPR wetlands. Using a combination of variables representing climate, land cover/land use and topography, we predicted wetland permanence class in the southern Boreal Forest, Parkland and Grassland natural regions of the Alberta PPR ( N = 40 000 wetlands). We show that while climate and land cover/land use were strong predictors of wetland permanence class, topography was as important, especially in the southern Boreal Forest and Parkland natural regions. Our misclassification error rates for the gradient boosting models for each natural region were relatively high (43–60) though our learning rates were low ( < 0.1) and our maximum tree depths shallow (5–7) to balance bias and overfitting. Clearly, factors in addition to climate, topography and land cover/land use influence wetland permanence class (i.e., basin size, depth, ground water connectivity, etc.). Despite classification errors, our results indicate that climate was the strongest predictor of wetland permanence class in the Parkland and Grassland natural regions, whereas topography was most important in the southern Boreal Forest Natural Region among the three domains we considered.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".