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Record W4309200029 · doi:10.1002/rra.4072

Modelling spatial and temporal variability of water temperature across six rivers in Western Canada

2022· article· en· W4309200029 on OpenAlexaffabout
Rajesh R. Shrestha, Jennifer C. Pesklevits

Bibliographic record

VenueRiver Research and Applications · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of VictoriaEnvironment and Climate Change Canada
Fundersnot available
KeywordsEnvironmental scienceClimate changeEcosystemHydrology (agriculture)Air temperatureDischargeSpatial variabilityHabitatWater qualityPhysical geographyClimatologyEcologyGeographyDrainage basinOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract Increasing river water temperature in response to the warming climate is concerning for water quality and ecosystem health of rivers. This study provides an assessment of the spatio‐temporal variability of the ongoing and potential future river water temperature ( T w ) change in western Canada. We use the air2stream model to reconstruct historical T w dataset for 17 stations across six rivers, and employ the reconstructed T w to analyze hydro‐climatic controls, trends, and sensitivities in relation to air temperature ( T a ) and discharge. Results provide insights on the contrasting summer (July and August) T w responses. While T w is primarily T a controlled for the northern rivers, discharge exerts increasing influence that approaches T a control for the southern rivers. Trends in T w are increasing and spatially varied, with significant increases and occurrences above the critical 18 and 20°C thresholds for the southern Fraser and Similkameen Rivers. A sensitivity analysis indicated 0.5–1.5°C T w increases for a 2.0°C T a increase, and 0.2–0.6°C T w increases for a 20% summer discharge decline. Overall, the results provide critical information for understanding the river ecosystem health, such as cold‐water species habitat.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.708

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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 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

Citations13
Published2022
Admission routes2
Has abstractyes

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