Hydrology, rather than wildfire burn extent, determines post‐fire organic and black carbon export from mountain rivers in central coastal California
Bibliographic record
Abstract
Abstract Coastal mountain rivers export disproportionately high quantities of terrestrial organic carbon (OC) directly to the ocean, feeding microbial communities and altering coastal ecology. To better predict and mitigate the effects of wildfires on aquatic ecosystems and resources, we must evaluate the relationships between fire, hydrology, and carbon export, particularly in the fire‐prone western United States. This study examined the spatiotemporal export of particulate and dissolved OC (POC and DOC, respectively) and particulate and dissolved black carbon (PBC and DBC, respectively) from five coastal mountain watersheds following the 2020 CZU Lightning Complex Fires (California, USA). Despite high variability in watershed burn extent (20–98%), annual POC, DOC, PBC, and DBC concentrations remained relatively stable among the different watersheds. Instead, they correlated significantly with watershed discharge. Our findings indicate that hydrology, rather than burn extent, is a primary driver of post‐fire carbon export in coastal mountain watersheds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| 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 teacher head, 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".