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Record W7029301440

Investigating Subsurface Flow Delivery to a Small High Arctic River

2019· dissertation· en· W7029301440 on OpenAlexfundno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2019
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersArcticNet
KeywordsChannelizedSubsurface flowArcticHydrology (agriculture)WatershedPermafrostHydraulic conductivityTemperate climateOutflow
DOInot available

Abstract

fetched live from OpenAlex

Preferential flow pathways and hydraulic gradients along the hillslope-floodplain-channel continuum are dominant controls on the delivery of baseflow, stormflow and solutes to channels in temperate systems. Arctic systems are increasingly being shown to possess similar delivery mechanisms, however the topic is understudied, acutely so in the High Arctic. This work assesses the nature of delivery mechanisms in the High Arctic over the thaw season and in response to rainfall at the Cape Bounty Arctic Watershed Observatory (CBAWO). Two locations of persistent preferential subsurface inputs were observed over a 200 m section the study reach, one at the outlet of a previously active channel through the floodplain, and another at the outlet of a channelized sub-catchment. During baseflow, these inputs provided solute-rich, relatively cold water to streamflow. During stormflow, subsurface inputs remain active and in the case of the sub-catchment, there was evidence for increased flux of pre-event soil water. Over the 320 m study reach, heterogeneity in the nature of hydraulic gradients, in how gradients developed over the season, and in how gradients responded to rainfall was observed. Two monitored hydraulic gradients exhibited reversal of inferred flow direction; one due to rainfall and the other as the thaw season progressed. Economic feasibility of hydrological investigations concerning total dissolved solids and electrical conductivity (EC) becomes an issue when spatial and temporal scales increase, especially when the objective is to capture the variability inherent within aquatic systems. Chapin et al. (2014) presented a modified version of the Onset HOBO Pendant waterproof temperature and light logger with the ability to assess relative EC, however it was not dimensional EC (i.e. µS/cm). This work demonstrates these economical loggers can be calibrated and applied to quantify EC in the field with accuracies comparable to commercial loggers.

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 score0.974
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.182
Teacher spread0.166 · 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
Published2019
Admission routes1
Has abstractyes

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