Evaluating the development and use of a rapid wetland assessment tool (ABWRET-A) in policy implementation in Alberta, Canada
Bibliographic record
Abstract
As wetlands around the world are being lost, policies are implemented to help protect further destruction and loss of valuable services that wetlands provide. In Alberta, wetland policy has been put in place with the goals of protecting the most valuable wetlands and replacing necessary loss of wetlands to maintain functional value. To help the policy meet its objectives, the Alberta Wetland Rapid Evaluation Tool-Actual (ABWRET-A) was developed and implemented in Alberta’s settled area in 2015 as a standardized way to give a value score via functional assessment to any wetland in the province, with the hopes that the most valuable wetlands will be conserved. These assessment tools are in constant need of review and improvement to make sure they are helping meet policy goals. \nI assess biases made in the selection for ABWRET-A calibration wetlands and determine how these biases affect ABWRET-A scoring to determine if subsequent scores provided by this tool are over or under estimating wetland value. I also assess the wetlands that underwent ABWRET-A evaluation and were drained or filled in under a permit in the 1.5 yr after ABWRET-A implementation in Alberta’s settled region to determine whether they mirror the calibration wetlands. \nI found that the calibration dataset comprised larger, more permanently ponded wetlands distributed closer to roads than the general wetland population. I also found that the calibration dataset included fewer bogs and more fens. I found that larger wetlands and wetlands classified as fens received higher ABWRET-A scores, whereas wetlands close to roads received lower scores. Consequently, I surmise that the scores being given out since ABWRET-A’s implementation are likely underestimates. This is corroborated by a lower distribution of scores in the wetlands permitted for drainage than policy recommends. The wetlands being targeted for permitted loss were also smaller, more road-proximate, and concentrated around major cities, implying permanent regional loss of those wetlands and their functions. \nBased on these findings, I make suggestions for improving ABWRET-A, including adding calibration sites to better capture the natural variability of wetlands in the area to improve ABWRET-A’s accuracy in estimating relative wetland value.
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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 | 0.040 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".