Interplay of Seasonal Hydrology and Landscape Topography Drives Black Carbon Export in the Fraser River
Bibliographic record
Abstract
Abstract Fire is an important driver of carbon cycling across terrestrial and aquatic ecosystems, but global fire regimes are changing. Black carbon (BC), a product of biomass burning, is more environmentally persistent than its parent biomass carbon and cycles differently than bulk organic carbon. This study aims to refine understanding of the environmental drivers of BC flux from land to ocean through year‐long measurement of BC in the Fraser River in British Columbia, Canada. The Fraser River’s environmental context is distinct from systems that currently form the basis of understanding of BC export, characterized by highly seasonally variable hydrology, and with its basin spanning diverse ecosystems from glaciated mountainous regions to dry flatlands. We found that the Fraser River exported 18,765 ± 2,734 Mg yr−1 of BC, with dissolved black carbon (DBC) comprising 3.3 ± 0.9% of annual dissolved organic carbon (DOC) flux, both lower than previous estimates would suggest. Strong seasonal variation in the DBC content of DOC and BC aromaticity were measured in the Fraser River. This reveals the importance of seasonal hydrology in the export of different pools of BC and indicates that seasonality and hydrologic regime should be given more consideration in future estimations of global riverine BC flux. These findings bring to light the importance of seasonality, hydrology, and basin topography in BC transport, with implications for global carbon cycles in a changing climate.
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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 | 0.001 |
| 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) | 0.002 | 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".