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Record W2783956520

Nitrogen Dynamics in a Harvested Rocky Mountain Catchment

2018· dissertation· en· W2783956520 on OpenAlexfundaboutno aff
David M. Stewart

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Agriculture and ForestryAlberta Environment and Parks
KeywordsGeographyDrainage basinEnvironmental scienceEcologyHydrology (agriculture)GeologyBiologyCartographyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

While the impacts of forest harvesting on nitrogen (N) are largely well documented, changes to watershed N following harvesting are difficult to characterize within steep, mountainous terrain creating uncertainty for downstream ecosystem and public health. For drinking water supplies, increases in source water N can reduce disinfection efficiency and increase oxidant demand. In conjunction with increases in other nutrients, changes in source water N concentrations and species can affect the trophic status of drinking water supplies, potentially leading to further treatability challenges including cyanobacterial blooms that have the potential to lead to service disruptions. Thus, an understanding of changes in key nutrients like N is critical for source water protection and management in the drinking water industry, particularly as disturbances occur in key source watersheds. Mountainous terrain can create different hydro-climatic regions that govern watershed export of N after forest harvesting. Physiographic features such as hillslope positioning and aspect can not only create strong spatial variation in, but also be used to categorize radiation, temperature, and nutrient turnover therefore governing post-disturbance catchment exports of N. \nThe broad goal of this research was to explore the role of physiography (hillslope positioning and aspect), on regulating post-harvesting N production and transport into streams in the eastern slopes of the Rocky Mountains in Alberta, Canada. A combined approach employing the measurement of key soil N species (laboratory analysis), N availability (ion exchange membranes), pore water N (suction lysimeters), and streamwater N species (historical data) allowed for a robust insight into how different fractions of N behave both spatially, and temporally. \nHillslope positioning (upper vs riparian) and aspect (north- and south-facing) were found to be important factors in N cycling. South-facing riparian zones showed greater potential nitrification, NH4+-N, NO3--N, available NH4+-N, available NO3--N, pH, TC, TN, and C/N ratios, and lower potential ammonification than any other locations investigated suggesting that these sites are more biogeochemically active and may contribute more to streamwater N than north-facing riparian zones. Compared to other types of forest disturbance (i.e. wildfire) that are common in nearby watersheds, clearcut harvesting did not produce a large impact on hillslope soil N availability or watershed scale production of N in 2016 (1st full growing season after harvesting). While a short transient pulse of streamwater NO3-, TDN, and TN was observed during a short series of rainstorms in the 3 to 4 spring months after harvesting, no harvest-associated effects on stream N export dynamics were detectable prior to, or 1.5 years afterwards. These findings support the notion of high potential watershed and ecosystem resistance to harvest impacts on N regimes in this region.

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)0.000
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 score0.864
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.186
Teacher spread0.180 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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