New mapping techniques to estimate the preferential loss of small wetlands on prairie landscapes
Bibliographic record
Abstract
Abstract Reliable estimates of wetland loss require improved wetland inventories and effective monitoring programmes. The Prairie Pothole Region of North America is experiencing rapid urban, agricultural and economic development, which places wetlands at risk, especially small geographically isolated wetlands. This loss is concomitant with a loss of ecosystem services. To improve upon current wetland inventories, a method for mapping wetlands using an automated object‐based approach was developed for a regional watershed in Alberta. The method improves upon existing wetland mapping methods by effectively mapping small wetlands and better capturing the convolution of wetland edges. This approach uses digital terrain objects derived from light detection and ranging data, from which 130 157 wetlands were identified. Wetland loss estimates (% number and % area) were obtained by applying a wetland area versus frequency power‐law function to the wetland inventory. We estimated a 16.2% historic loss of wetland number and a 2.6% loss of wetland area, with the size of these lost wetlands <0.04 ha. The improved techniques for mapping wetland loss and estimating wetland loss provide a more accurate representation of the magnitude of wetland loss in the Prairie Pothole Region. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".