Improved estimation of wetland cover in the western Canadian boreal forest
Bibliographic record
Abstract
The Alberta Wetland Inventory (AWI), which is used in a variety of applications across the province to estimate wetland cover from aerial photographs, detected only 34% of confirmed wetland field plots in boreal forest watersheds in the Swan Hills of Alberta. Given the association between wetland cover and runoff and surface water chemistry in western Canadian boreal forest (Boreal Plain) watersheds, accurate quantification of wetland cover is critical to efforts to model hydrologic processes and water quality. Therefore, as a component of the Forest Watershed and Riparian Disturbance (FORWARD) Project, the Wetland Inventory and Identification Tool (WIIT) was developed and successfully detected 81% of the wetland field plots. Application of both models across a variety of landscapes in the boreal forest of Alberta demonstrated that wetland cover estimates were 1.5 times higher with the new WIIT model than with AWI. Also, WIIT identified polygons that were both smaller and contained taller trees than those identified by AWI, indicating that this computer model may be more effective than wetland identification methods that use only aerial photography. Results of this study show that careful interpretation of aerial photographs at the 1:15,000 scale, coupled with ground truthing and computer models, can provide an accurate means of identifying wetlands on Boreal Plain landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".