Determining the effects of wildfire on sediment sources using 137Cs and unsupported 210Pb: the role of landscape disturbances and driving forces
Bibliographic record
Abstract
Wildfires represent one of the major natural disturbances within forested landscapes and have potential implications for the quality and function of downstream aquatic ecosystems. This study aimed to determine if a wildfire in a mountainous, forested watershed in British Columbia, Canada, caused a change in the dominant sediment source in the immediate 1–2 years following the wildfire, and if the sediment sources changed over the medium term (3–7 years) as the landscape recovered. Source materials (surface soil, subsurface soil and channel bank material) and fluvial (suspended and channel bed) sediment samples were collected over the period 2004 to 2010 from a watershed burnt by a wildfire in 2003, and from an adjacent watershed that was not impacted by the fire. Samples were analysed for the fallout radionuclides (FRNs) caesium-137 (137Cs) and unsupported lead-210 (210Pbun). An unmixing model was used to calculate the relative source contributions of the fluvial sediment samples. 137Cs and 210Pbun were concentrated in the upper layers of surface soils in both watersheds and were statistically different to concentrations in subsurface and channel bank material. In the burnt watershed, FRN concentrations were greatest in the ash layer. Sediment sources as determined by the unmixing model were 100 % subsurface/channel bank material in the unburnt watershed, while in the burnt watershed 8.5 ± 2.5 % was derived from surface soils. In both watersheds, there were no major changes in the relative contributions from surface soil and from subsurface/channel bank material over the period 2004 to 2010. Thus, while the wildfire did cause a change in sediment sources, it was fairly subtle and did not conform to the effects following wildfire described for other studies in contrasting environments, which typically document a major increase in hillslope contributions relative to channel bank sources. There was a limited response in terms of fine-grained sediment sources (and also sediment fluxes) in the burnt watershed. The reason for this muted response to a severe wildfire is likely to be the lack of precipitation, especially winter precipitation and the associated snowmelt, in the first year following the wildfire. Thus while the landscape was primed for erosion and sediment transport, the lack of a driving force meant that there was a limited immediate post-fire sediment response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".