A wetland permanence classification tool to support prairie wetland conservation and policy implementation
Bibliographic record
Abstract
Abstract Wetland permanence, the duration and frequency that surface water is present, affects biological communities and whether wetlands are protected under legislation in some jurisdictions. Wetland drainage in the Prairie Pothole Region (PPR) has changed the distribution of wetlands because smaller and more temporary wetlands are more likely to be drained. This change in distribution affects biodiversity and other wetland ecosystem services. In Manitoba, Canada, wetlands are treated differently under the Water Rights Act based on permanence classification and can either be drained with a simplified registration (temporary and ephemeral wetlands), drained with a permit requiring mitigation (seasonal wetlands), or are protected from drainage (semipermanent and permanent wetlands). To facilitate implementing a conservation program targeting the most vulnerable wetlands, we built a classification model using LiDAR and Sentinel‐2 data (1312 training observations). Our random forest model had 73% accuracy on 563 test observations and is applicable across the agricultural region of southwestern Manitoba. We predicted the wetland permanence class of 365,499 wetlands and built an online tool to help practitioners implement a conservation program that pays producers to conserve temporary and ephemeral wetlands. Our approach is applicable elsewhere in the PPR and other regions with variation in wetland permanence.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".