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Record W2944378958 · doi:10.1002/lno.11180

Episodic hypoxia in the western basin of Lake Erie

2019· article· en· W2944378958 on OpenAlexafffund
Aidin Jabbari, Josef Daniel Ackerman, Leon Boegman, Yingming Zhao

Bibliographic record

VenueLimnology and Oceanography · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryQueen's UniversityUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Natural Resources and Forestry
KeywordsHypolimnionHypoxia (environmental)UpwellingStructural basinEnvironmental scienceOceanographyStratification (seeds)Thermal stratificationHydrology (agriculture)GeologyEutrophicationEcologyOxygenBiologyThermoclineGeomorphologyNutrientChemistry

Abstract

fetched live from OpenAlex

Abstract Hypoxic conditions continue to be an environmental concern in lakes, including those with shallow and well‐mixed basins, such as the western basin of Lake Erie, in which hypoxia is not anticipated. We investigated the dynamics and causes of hypoxia using field measurements at two locations in the western basin during the late summer of 2017. Two hypoxic events (dissolved oxygen [DO] concentrations < 2 mg L −1 ) were recorded that were caused by upwelling of hypolimnetic water from the central basin of the lake following winds from the south and southwest. In this case, instantaneous stratification occurred when cool central basin water (i.e., 15.7°C) intruded as a 2.5‐m‐thick layer above the bottom under the warm western basin waters (i.e., 23.9°C). A third hypoxic event, which was associated with more typical thermal stratification from atmospheric warming, occurred during a calm and warm period near the end of the deployment. In this case, we observed a continuous decline in hypolimnetic DO from ≈ 8 to < 5 mg L −1 , which likely declined to < 2 mg L −1 in 14 d using a one‐dimensional model. Interbasin exchange flows generated instantaneous hypoxia multiple times within a year and were the dominate cause (63% of 11 cases) of hypoxia identified during August fishing trawls in the study area over the past 30 yr. Results of this work should help with the prediction and understanding of hypoxia in lakes with multiple basins, which will be informative for water quality and fisheries management.

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 score0.037
Threshold uncertainty score0.534

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 head0.006
GPT teacher head0.191
Teacher spread0.186 · 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

Citations53
Published2019
Admission routes2
Has abstractyes

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