Comparative impacts of fire and forest harvesting on water quality in Boreal Shield lakes
Bibliographic record
Abstract
Water quality was monitored in Boreal Shield lakes for 3 years following their simultaneous impact by clearcut logging or wildfire. Seventeen similar undisturbed lakes served as references. Dissolved organic carbon (DOC) and the light attenuation coefficient (ε PAR ) were up to threefold higher in cut lakes than in reference and burnt lakes. Compared with median values for reference lakes, cut and burnt lakes had higher concentrations of total phosphorus (TP) (two- to three-fold), total organic nitrogen (TON) (twofold), and K + , Cl - , and Ca 2+ (up to sixfold). NO 3 - and SO 4 2- concentrations were up to 60- and 6-fold higher, respectively, in burnt lakes than in reference and cut lakes. In most cases, impacts were directly proportional to the area harvested or burnt divided by the lake's volume or area. These simple models correctly predicted the changes observed in three lakes harvested during the study. Some of the ob served effects occur on different time scales. Mobile ions released by fire (K + , Cl - , SO 4 2- , NO 3 - ) or harvesting (K + , Cl - , some DOC) are rapidly flushed out of the watershed (50% decrease in 3 years). Other constituents or properties (TP, TON, DOC, ε PAR , Ca 2+ , Mg 2+ ) show little change or are still increasing after 3 years and will take a longer time to reach normal levels.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".