Spatial and temporal shifts in dissolved organic matter composition across a burned stream network
Bibliographic record
Abstract
Increasing wildfire activity can impact the global carbon cycle, aquatic ecosystem health, and drinking water treatment through alterations in aquatic dissolved organic matter (DOM) composition. However, uncertainty remains about the spatial and temporal variability in wildfire effects on DOM composition. We sought to improve understanding of how burn severity affects stream DOM and how weather, hydrology, and landscape factors contribute to variability in post-fire DOM responses across space and time. Following a large 2020 wildfire in Oregon, USA, we collected water samples to quantify dissolved organic carbon and DOM optical properties at 129 stream sites across the fire-affected stream network. Sampling was repeated across seasonal hydrologic conditions to capture variation in hydrologic pathways and organic matter sources. We developed a PARAFAC model using excitation-emission matrices (EEMs) and used spatial stream network (SSN) models to determine how DOM composition changed across the stream network with burn severity. The greatest shifts in DOM composition were observed during the dry and wetting seasons, with an increase in aromatic DOM at higher burn severities. In contrast, an increase in protein-like DOM was observed during the wet season at higher burn severities. Drainage area, 31-day and 1-day antecedent precipitation, and baseflow index impacted the relationship between DOM composition and burn severity, which could partially explain the variability in post-fire DOM responses. Our study contributes a mechanistic understanding of how wildfire impacts DOM sources and composition, which is critical to predicting wildfire effects on aquatic biogeochemical cycling and preserving ecosystem health and source 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".