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Record W4319054976 · doi:10.1016/j.ejrh.2022.101301

Estimation of groundwater contributions to Athabasca River, Alberta, Canada

2023· article· en· W4319054976 on OpenAlexafffundabout
Hyoun‐Tae Hwang, Andre R. Erler, Omar Khader, Steven J. Berg, Edward A. Sudicky, Jon P. Jones

Bibliographic record

VenueJournal of Hydrology Regional Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsSuncor Energy (Canada)University of Waterloo
FundersImperial Oil LimitedSuncor Energy Incorporated
KeywordsGroundwaterSurface waterHydrology (agriculture)Groundwater flowEnvironmental scienceStructural basinDrainage basinGroundwater dischargeWater balanceGroundwater modelStreamflowGeologyAquiferGeographyEnvironmental engineeringGeomorphology

Abstract

fetched live from OpenAlex

The Athabasca River Basin (ARB), Canada This study investigates the dynamic interactions that occur between surface water and groundwater systems within the Athabasca River Basin (ARB). The integrated surface-subsurface model of the ARB was first calibrated under monthly normal transient flow conditions to observed surface water and groundwater data, after which the model was forced with monthly average transient data to evaluate model performance. From these results, groundwater contribution to the Athabasca River system was calculated using simulated surface water-groundwater exchange fluxes. These estimates are compared to those obtained from an isotope-based balance analysis which also estimated groundwater contribution to the Athabasca River. The results from this study suggest that the groundwater system in the basin has a high degree of interaction with the surface water system. Specifically, the groundwater contribution to the surface water system along the Athabasca River ranges from 34% (high flow season) to 63% (low flow season) with an overall average annual groundwater contribution of 45%. These results indicate that the groundwater system needs to be considered when analyzing potential climate change impacts on future water availability and extreme hydroclimatic events.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

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 head0.269
Teacher spread0.253 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2023
Admission routes3
Has abstractyes

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