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Enregistrement W4388407905 · doi:10.24124/2023/59430

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

2023· dissertation· en· W4388407905 sur OpenAlexaffabout
Kristen Kieta

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueFire effects on ecosystems
Établissements canadiensUniversity of Northern British Columbia
Organismes subventionnairesnon disponible
Mots-clésSedimentEnvironmental scienceWatershedTributaryRiparian zoneHydrology (agriculture)STREAMSHabitatEcosystemPopulationAquatic ecosystemDrainage basinEcologyGeographyGeologyOceanography

Résumé

récupéré en direct d'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.,

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,158
Score d'incertitude au seuil0,315

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,231
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

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