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Record W4404386911 · doi:10.1002/eco.2739

Pumping Groundwater to Create Cold‐Water Thermal Refuges in Warming Rivers

2024· article· en· W4404386911 on OpenAlexafffundabout
Kathryn A. Smith, Barret L. Kurylyk

Bibliographic record

VenueEcohydrology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaGeological Society of America
KeywordsEnvironmental scienceGroundwaterHydrology (agriculture)Water resource managementGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Thermal refuges in rivers and streams provide critical habitat for cold‐water species during periods of thermal stress. In this study, we created a new cold‐water thermal refuge by pumping cool groundwater to a warm coastal river in Nova Scotia, Canada. Thermal infrared imagery revealed a notable thermal plume, measuring approximately 55 m 2 at the water surface during low‐flow conditions, with mixing limited by the installation of a flow deflector. Above‐water and underwater cameras recorded several fish utilizing the created cold‐water plume during periods of high ambient river temperatures (up to 30°C). Thermal numerical modelling was conducted to interpret the field data and assess the impact of alternative designs and conditions. Model results revealed that the extent of the created thermal plume substantially increased (+202%) with the use of a deflector and that the plume size was controlled by several factors including the river flow rate and temperature, the pumping rate and the groundwater temperature. The study findings demonstrate the efficacy of creating cold‐water habitat in the face of a warming climate and lay the foundation for future proactive thermal management strategies aimed at maintaining thermal diversity in warming rivers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.999

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations12
Published2024
Admission routes3
Has abstractyes

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