Determination of sediment sources following a major wildfire and evaluation of the use of color properties and polycyclic aromatic hydrocarbons (PAHs) as tracers
Bibliographic record
Abstract
Purpose: This research aimed to determine if a severe wildfire caused changes in the source of sediment being delivered to downstream aquatic systems and evaluate the use of polycyclic aromatic hydrocarbons (PAHs) and color properties as tracers. Methods: Sediment samples were collected from 2018 to 2021 in three tributaries impacted by the 2018 Shovel Lake wildfire and from two sites on the mainstem of the Nechako River, British Columbia. Source samples were collected from burned and unburned soils as well as from channel banks and road-deposited sediment. Samples were analyzed for color properties and for the 16 US Environmental Protection Agency priority PAHs. After statistical tests to determine the conservatism and ability to discriminate between sources by the tracers, the MixSIAR unmixing model was used, and its outputs were tested using virtual mixtures. Result: In the tributaries, burned topsoil was an important contributor to sediment (up to 50%). The mainstem Nechako River was not influenced as significantly by the fires as the greatest contributor was banks (up to 89%). The color properties provided more realistic results than those based on PAHs. Conclusion: In smaller watersheds, the wildfire had a noticeable impact on sediment sources, though the impacts of the fire seemed to be diluted in the distal mainstem Nechako River. Color tracers behaved conservatively and discriminated between contrasting sources. Due to their low cost and reliability, they should be considered more widely. While PAHs did not work in this study, there are reasons to believe they could be a useful tracer, but more needs to be understood about their behavior and degradation over time. Supplementary Information: The online version contains supplementary material available at 10.1007/s11368-023-03565-0.
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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 | 0.001 | 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 | 0.000 |
| 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 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".