Forest harvest impacts on water quality and aquatic biota on the Boreal Plain: introduction to the TROLS lake program
Bibliographic record
Abstract
Eleven headwater lakes in Alberta's Boreal Plain were monitored for nutrients and plankton 2 years before and 2 years after variable watershed harvesting (harvesting mean 15%, range 0-35%). After harvesting, variations in annual precipitation resulted in lake water residence times that differed by an order of magnitude from one year to the next. During the first posttreatment year, total phosphorus concentrations increased (overall 40%) in most lakes; however, response was most consistent in lakes that were shallow and the water column mixed or weakly thermally stratified. Chlorophyll a, cyanobacteria (Aphanizomenon-Anabaena), and cyanotoxins (microcystin-LR) increased after harvesting, primarily in shallow lakes. Zooplankton abundance and biomass decreased after harvesting, particularly in stratified lakes where edible phytoplankton biomass declined. In the weakly or nonstratified lakes, declines in zooplankton biomass were associated with higher cyanobacterial biomass and cyanotoxins. Posttreatment change in total phosphorus concentration was strongly related to weather (greatest response in a wet year) and relative drainage basin size (drainage basin area to lake volume, r2 = 0,78, P << 0,01). There was no evidence that buffer strip width (20, 100, and 200 m) influenced lake response. These results suggest that activities within the entire watershed should be the focus of catchment-lake interactions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".