Current approaches to wetland status and trends monitoring in prairie Canada and the continental United States of America
Bibliographic record
Abstract
Canada and the United States share a common concern for the North American wetland resource. Despite their ecological and social importance, comprehensive and scientifically sound data on the national status and trends of Canadian wetlands are lacking. Conversely, in the United States, a nationwide comprehensive inventory and monitoring program providing status and trends information is currently implemented. Canada and the United States recognize that national policy and management questions about wetland resource status rely on scientifically based processes to periodically measure wetland status and trends. Both countries have developed monitoring schemes independently. Program similarities include the selection of a probabilistic sample design and a common definition of wetland loss. Program similarities and the shared concern over the North American wetland resource should act as a catalyst for further cross-border cooperation in the areas of wetlands inventory and monitoring. National wetland monitoring in Canada could likely be accomplished through a program similar to the currently operational US Fish and Wildlife Service (USFWS) and Canadian Wildlife Service (CWS) programs. This paper reviews the operational programs implemented by the USFWS to monitor wetlands at a national scale and the CWS to monitor wetlands in prairie Canada for the purpose of providing suggestions for the development of a national wetlands monitoring program in Canada.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".