A paleolimnological investigation of the effects of forest fire on lake water quality in northwestern Ontario over the past ca. 150 years
Bibliographic record
Abstract
Fire is an important mechanism of disturbance in boreal ecosystems; however, the effects of fire on lake ecosystems are still not well understood. This study provides a detailed assessment of the impacts of fire on the limnology of a small oligotrophic lake (Lake 42), located approximately 200 km northwest of Thunder Bay, Ont. The study lake is characterized by a small drainage ratio (watershed area : surface area) and a relatively long water residence time. Age establishment and fire scar analyses determined that at least one, and perhaps two, major fires had burned to the lake's shoreline in the past ca. 150 years. Using a paleoecological approach, diatoms were examined in a 210Pb-dated sediment core. Following watershed fires, minimal changes were noted in the diatom species assemblage. These findings may be explained by the low sedimentation rates and small drainage ratio of the study lake, although other studies suggest that the biological response may be minimal compared with physicalchemical responses in some ecosystems. Beginning in the early 1980s, however, distinct changes were noted in the species assemblage and in diatom-inferred total phosphorus. Our findings suggest that the study lake may be more sensitive to precipitation inputs of nutrients than to inputs resulting from watershed disturbances.Key words: paleolimnology, diatoms, forest fire, water quality.
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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 | 0.001 | 0.001 |
| 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".