Can alluvial landforms attenuate post-wildfire lake sedimentation rates and external phosphorus inputs?
Bibliographic record
Abstract
Severe large-scale landscape disturbance in forested source water regions by wildfire can increase the transfer of fine sediment and associated phosphorus (P) to aquatic systems. Notably, the subsequent mobilization of sediment-associated P to receiving surface waters can increase primary productivity in lakes and severely degrade water quality. The goal of this research was to examine abiotic controls on the form and mobility of particulate phosphorus (PP) in benthic sediment of an oligotrophic lake in Waterton Lakes National Park (WLNP), Alberta. In 2017, the Kenow wildfire severely burned an area of 35 000 ha which increased the transfer of pyrogenic materials to receiving streams in WLNP. The temporal distribution and concentration profiles of PP forms (non-apatite inorganic P, apatite P, organic P) and P mobility (equilibrium phosphorus concentration, EPC0) and major elements in a sediment core collected from Lower Waterton Lake (LWL) were evaluated to assess the potential of post wildfire fine sediment inputs to influence P concentrations in the overlying water column, and the potential internal loading legacy effect that wildfires may have on lake P-dynamics. Despite elevated PP levels observed in fire impacted tributaries that flow into LWL, the particulate P fractionation data show that post-fire P loading from pyrogenic materials to the lake was extremely low and only very minor post-wildfire changes in TPP speciation were observed. The presence of a large alluvial fan impeding river flow, coupled with the ingress of fine sediment in gravel bed rivers and adjacent floodplains, most likely attenuated the influx of pyrogenic materials into LWL. The mobility of PP in lake bottom sediment corresponded with historical landscape disturbances (e.g., flooding), which are likely associated with the remobilization of deposited pyrogenic materials in the alluvial fan, demonstrating a fire-flood sequence. Although the data presented in this thesis suggests that wildfire will have a minimal effect on the internal loading of P in LWL, it is increasingly acknowledged that non-stationarity due to changing climate may produce flow conditions which will resuspend and deliver pyrogenic materials to the lake, potentially creating a post-wildfire legacy effect. This thesis provides new knowledge regarding the effects of alluvial landscape forms on the post-wildfire delivery and mobility of PP into critical source water areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".