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Record W4396546536 · doi:10.1038/s43247-024-01407-6

Extreme hydrometeorological events induce abrupt and widespread freshwater temperature changes across the Pacific Northwest of North America

2024· article· en· W4396546536 on OpenAlexafffund
Stephen J. Déry, Eduardo G. Martins, Philip N. Owens, Ellen L. Petticrew

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Northern British Columbia
FundersUniversity of Northern British Columbia
KeywordsHydrometeorologyClimatologyEnvironmental scienceFlooding (psychology)Heat waveClimate changeOceanographyGeographyGeologyPrecipitationMeteorology

Abstract

fetched live from OpenAlex

Abstract The Pacific Northwest of North America experienced four extreme hydrometeorological events during 2021 including intense cold waves in mid-February and late December, the record-setting June heat dome, and catastrophic floods caused by two November atmospheric rivers. While the synoptic-scale patterns and terrestrial hydrological responses to these extreme events are well documented, scant information has been published on corresponding freshwater temperature responses. Here, we apply an observational database of hourly freshwater temperatures at 554 sites across the region to characterize their evolution during these four extreme hydrometeorological events. The two cold snaps and summer heat dome induced a general 1 °C decline and 2.7 °C increase, respectively, in water temperatures with subdued changes (+0.4 °C) during the mid-November floods. For 193 sites with long-term records, 478 daily maximum water temperatures were exceeded during the heat dome and 94 were surpassed during the flooding event, suggesting deleterious effects for water quality and aquatic species.

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 score0.368
Threshold uncertainty score0.515

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.024
GPT teacher head0.239
Teacher spread0.215 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

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