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Record W7037490753

Dissolved organic carbon concentration and character in northern hardwood-dominated headwater catchments: A paired-catchment investigation of legacy harvesting impacts

2023· dissertation· en· W7037490753 on OpenAlexfundaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolved organic carbonHydrology (agriculture)WatershedWater qualitySTREAMSTotal organic carbonDrainage basin
DOInot available

Abstract

fetched live from OpenAlex

The water quality of forested source water regions can be degraded by natural and anthropogenic landscape disturbances such as wildfires and forest harvesting, the latter an economically important primary industry in Canada and a proposed wildfire mitigation strategy. Harvesting practices can alter the chemistry and hydrologic connectivity of hillslope solute pools, thereby enhancing hillslope-stream transport and the downstream propagation of sediments and solutes, including those relevant to drinking water treatment operations such as dissolved organic carbon (DOC). Although many studies have evaluated the sub-decadal impacts of forest harvesting on the concentration, export, and character of stream DOC, less is known about the legacy (decadal-scale) impacts. The purpose of this thesis was to evaluate the legacy impacts of clearcut harvesting on the variability of stream DOC concentrations, export, and character at the Turkey Lakes experimental Watershed (TLW). Using a paired-catchment approach (unharvested reference vs. legacy (24 years post-) clearcut), inter- and intra-catchment variability in stream DOC concentrations and export was evaluated under a range of flow conditions. Stream DOC variability was related to the concentrations, spatial distribution, and hydrologic connectivity of hillslope solute pool DOC. Additionally, a subset of event-scale stream and hillslope solute pool samples were analyzed for DOC character using Liquid-Chromatography Organic Carbon Detection (LC-OCD). DOC character was expressed in terms of the specific UV absorbance at 254 nm (SUVA) and the relative contributions of LC-OCD-defined DOC fractions. Whereas stream DOC concentrations in the legacy clearcut catchment exceeded (+1.21 mg L-1) and differed significantly (p ≤ 0.05) from the unharvested reference catchment, inter-catchment differences in stream DOC export were inconsistent. No inter-catchment differences were observed in the DOC concentrations or hydrologic connectivity of the hillslope solute pools, despite the common association of these mechanisms with post-harvest increases in stream DOC concentrations. Significant (p ≤ 0.05) inter-catchment differences in the fractional composition of stream DOC were observed at the event-scale but may be related to the presence of a wetland near the outlet of the unharvested reference catchment, rather than a harvesting impact. Wetland position was identified as a key factor in the variability of both DOC concentration and character in the unharvested reference catchment. Overall, the results of this thesis suggest that while forest harvesting practices may result in long-term increases in stream DOC concentration in northern hardwood-dominated headwater catchments, the effects may be limited at decadal-scales and likely do not pose a reasonable threat to downstream drinking water treatment operations.

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 score0.025
Threshold uncertainty score0.050

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.008
GPT teacher head0.188
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
Published2023
Admission routes2
Has abstractyes

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