Threats and protection for peatlands in Eastern Canada
Bibliographic record
Abstract
Peatlands (or mires) are acidic freshwater wetlands that cover 4 million km2 (i.e., 3-4 %) of the planet’s surface, according to recent estimates. Although pristine peatlands are becoming rare in many European countries, most of the peatlands in Canada remain untouched. A recent estimate of the total area of peatlands in Canada is approximately 170 million ha. Precise statistics on peatland destruction and disturbance are difficult to obtain for the entire country, due to its large size. Here, we present detailed information on two provinces of eastern Canada : Québec and New-Brunswick. Approximately 120 000 ha of peatland in Québec have been flooded as a result of construction of hydroelectric dams, the main factor for peat-land lost in that province. It represents 1 % of the total peat-land area. For New-Brunswick, peat extraction is the main threat for peat-lands, with 6 800 ha of peat-land mined for horticultural peat which represents 5 % of the total peat-land resources of that province. Nevertheless, peat-lands in certain regions are strongly affected by human activities and conservation actions must be undertaken to assure equal representation of peatlands across the country. Manitoba, Nova Scotia, Prince Edward Island and New-Brunswick are the four provinces that have achieved the highest conservation rates with 25, 15, 12,5 and 11 % of their peatlands under protection, respectively. The situation is different in other provinces such as Québec, where 3,6 % of the total peatland area occurs in protected areas. Provincial legislation and protection plans need to be further developed in the future to attain international standards.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".