Discriminating fine sediment sources and the application of sediment tracers in burned catchments: a review
Bibliographic record
Abstract
Abstract Wildfire can cause substantial changes to runoff, erosion and downstream sediment delivery processes. In response to these disturbance effects, the main sources of sediment transported within burned catchments may also change. Sediment tracing offers an approach to determine the proportional contributions of fine sediment (typically <63 µm) from burned catchment sources. In this paper, we review the application of various sediment tracers to discriminate fine sediment sources following wildfire. Fallout radionuclides provide the most effective tracers for discriminating hillslope surface and sub‐surface (usually channel bank) sources. Previous tracer studies quantifying contributions from these sources after wildfire are based exclusively on radionuclides. The potential for fallout radionuclides to discriminate spatial sources defined according to burned and unburned areas depends on burn‐related changes in surface soil concentrations. Tracing of burned and unburned source areas will be problematic where most radionuclides in surface material are bound to ash rather than soil. Geochemical properties of surface soils are probably too susceptible to natural and burn‐related variability to consistently discriminate burn‐defined spatial sources. Mineral magnetic properties have shown potential for discriminating soil burned at different severities as well as unburned areas. More research is needed to assess the use of soil organic compounds as tracers of burned source areas. Linking fallout radionuclide‐based hillslope and channel source discrimination with process measurements and monitoring after fire can provide enhanced insight into fine sediment transfer and related water quality impacts. Adopting such integrated and multi‐scale approaches would contribute to improved understanding of hydrological and geomorphological responses to wildfire. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".