Watershed Impacts of Logging and Wildfire: Case Studies in Canada
Bibliographic record
Abstract
In Canada, disturbance of boreal forests has increased due to expanding anthropogenic activities, particularly forestry. A first attempt was made to evaluate impacts of forest harvesting and wildfire on changes in water quality and biota of lakes. We present case studies in two major geological and climatic subregions of the boreal ecozone in Canada: the Boreal Plain and the Boreal Shield. Responses of lake ecosystems to wildfire and logging differed. In upland Boreal Plain lakes, total phosphorus (P), inorganic nitrogen (N) and algal biomass were higher in lakes with burned watersheds, whereas, only total P increased in lakes with watershed logging. Logging on the Boreal Shield and wildfire in wetland-and permafrost-dominated watersheds on the northern Boreal Plain were associated with increases in dissolved organic carbon (DOC) and water colour, possibly causing light-limitation of algae in both regions, and a decrease in calanoid biomass in eastern Boreal Shield lakes. The number of water quality indicators affected by watershed disturbances was greater in the oligotrophic ecosystems of the Boreal Shield. The nutrient response of disturbed lakes was strongly related to lake drainage ratios: lakes with high drainage ratios had the strongest response to disturbance. Effects were also dependent on climate, wetland coverage, and regional lake characteristics. Morphometric, chemical, and biological indicators are recommended to monitor natural and anthropogenic watershed disturbances of aquatic ecosystems in Canada's Boreal forest.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".