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Record W4388407905 · doi:10.24124/2023/59430

Determining contamination and sources of sediment in response to recent and historical landscape disturbances

2023· dissertation· en· W4388407905 on OpenAlexaffabout
Kristen Kieta

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSedimentEnvironmental scienceWatershedTributaryRiparian zoneHydrology (agriculture)STREAMSHabitatEcosystemPopulationAquatic ecosystemDrainage basinEcologyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

Landscape level disturbances occur in nearly every watershed throughout the globe, and as the climate changes, these disturbances will continue to have a significant impact on terrestrial and aquatic ecosystems. Wildfire, timber harvesting, and agricultural expansion are only a few of these disturbances, but each uniquely impacts the sediment regime. While sediment is a necessary and beneficial input to streams and rivers, it also can have negative impacts on the aquatic ecosystem because it can carry contaminants and also be physically detrimental when it settles, clogging spawning habitat. All of these disturbances have occurred historically and in the present in the Nechako River Basin (NRB), a large, regulated watershed in north-central British Columbia, Canada. In the NRB, chinook and sockeye salmon and the Nechako White Sturgeon are species that have been declining in population, in part due to the clogging of their habitat by sand and fine sediment. One way to determine sources of sediment is by using the sediment fingerprinting technique, whereby sediment samples and samples from potential sources are collected and analyzed for a series of physical or biogeochemical properties, and the proportion of sediment coming from each potential source is identified using an unmixing model. After catastrophic wildfires in 2018, research was undertaken to determine the spatial and temporal contamination of soils and sediment by polycyclic aromatic hydrocarbons (PAHs), to determine if burned areas were contributing more sediment than unburned areas to tributaries and the Nechako River mainstem, and to determine the suitability of PAHs as a novel fingerprint. The results found that concentrations of PAHs in the burned soils were elevated immediately post-wildfire, but decreased significantly in subsequent years, and concentrations in sediments were very low. While PAHs were deemed to be non-conservative properties, unmixing modeling using colour showed that burned sources were an important contributor to the tributaries, but less so in the mainstem Nechako River. Agriculture is an important and growing industry in the NRB and is also an important source of sediment. Results from fingerprinting research undertaken in Murray Creek, an important watershed due to its proximity to spawning habitat, found that agriculture was the primary source of sediment in the basin, though channel banks were also important. While the intention was to use compound specific stable isotopes of long chain fatty acids to more specifically pinpoint agricultural fields that were contributing more sediment to the watershed, this semi-novel tracer was unable to discriminate between C3 plant types on a large scale. Taking the entire disturbance regime of the NRB into account, a broader scale fingerprinting study found that sources of sediment are tributary specific, though banks and agriculture were consistently most important. This study also identified that the predicted shift to a rain dominated watershed and earlier freshet will lead to increased potential for erosion from various sources, and that increased incidence of wildfire followed by heavy precipitation may increase sediment loads. Therefore, a number of management changes are suggested, including improving farming practices, post-wildfire landscape rehabilitation, and altering water release practices.,

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.231
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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