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Record W7154406584 · doi:10.36939/ir.202604141431

Seasonal landscape variability in drivers of dissolved organic carbon concentrations in headwater streams draining forested catchments

2018· dissertation· W7154406584 on OpenAlexaboutno aff
Adrienne Ducharme

Bibliographic record

Venuenot available
Typedissertation
Language
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDissolved organic carbonAntecedent moistureHydrology (agriculture)STREAMSDrainage basinCatchment hydrologyAquatic ecosystemEcohydrologyGroundwaterEcosystem

Abstract

fetched live from OpenAlex

Dissolved organic carbon (DOC) is an important component of aquatic ecosystems, as it influences many parameters within freshwater ecosystems and is transported from upland forests and soils through groundwater and stream flow to aquatic systems via hydrological flowpaths. Recent increases in 'lake browning' have been investigated but the mechanisms underpinning these increases are not entirely understood. One hypothesis is that climate-driven changes in hydrology may result in changes in DOC export from forested catchments. This thesis explored how climate-driven variability in hydrological flowpaths influenced catchment DOC concentrations in headwater streams draining three forested Precambrian shield catchments in northwestern Ontario. The main objectives of this research were to: 1) examine long-term annual and seasonal patterns in DOC concentrations across catchments with varying topography to determine relationships between DOC concentration and hydrological drivers; and 2) investigate DOC transport dynamics during high-flow events to assess how differences in flowpaths and hydrological condition result in variability in stream DOC concentrations among catchments and seasons. Chapter 2 explored patterns and trends of DOC concentration and export among catchments and seasons and explored inter-catchment variability in l1atterns ofDOC concentrations by examining the relationships with chemical and hydrological parameters. Chapter 3 explored how catchment characteristics and hydrological conditions interact to affectDOC concentrations by investigating transport dynamics during high-flow events and by evaluating relationships between DOC and varying antecedent moisture conditions (AMCs). Long-term data demonstrated that differences in DOC concentration among catchment and seasons were driven by differences in flowpath, antecedent moisture and discharge. The wetland-dominated catchment had the highest concentrations of DOC and exhibited inverse relationships between DOC concentrations and discharge; DOC sources were proximal to the stream and transport within this catchment was typically source-limited. The two upland catchments displayed nearly opposite results compared to the wetland-dominated catchment: DOC concentrations were lower, relationships were positive between DOC and discharge, ROC sources were distal to the stream and transport within these two upland catchments was generally transport-limited. This thesis suggests that the hydrologic pathways, especially during high-flow events, are different for each of the study catchments and were activated at different times depending on size, proportion of wetland and AMCs within each catchment.

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 score0.422
Threshold uncertainty score0.839

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.007
GPT teacher head0.230
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

Citations0
Published2018
Admission routes1
Has abstractyes

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