Cumulative effects of fire in the Fraser River basin on freshwater quality and implications for the Salish Sea
Bibliographic record
Abstract
The movement of water from land to the ocean serves as a major biogeochemical link between terrestrial and marine systems, through which terrestrial disturbances can impact freshwater quality and coastal oceans. Wildfire is a major terrestrial disturbance, however its influence on water quality in large freshwater systems and the ocean is understudied. As anthropogenic pressures change fire regimes globally, it is important that this connection is better understood. The Fraser River's basin has significant wildfire history, and the Fraser River has major influence on its receiving waters of the Salish Sea, making this an ideal system in which to investigate the influence of fire on water quality from freshwater to the ocean. This study assessed cumulative impacts of wildfire on Fraser River water quality using historical water quality and fire data. Wildfire in the Fraser River basin explained up to 16.3 % of variance in water quality, and fires burning closer to major waterways had immediate influence on water quality, while farther away wildfires had a delayed influence on water quality. For water quality variables of particular importance in the Salish Sea, wildfire was linked to short term decreases in the riverine concentrations of some constituents, and delayed increases in the concentrations of nearly all assessed water quality variables. These findings have implications for coastal ocean productivity and contamination, and identify fire as an important agent of biogeochemical cycling between land and ocean.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".