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Record W2332525138 · doi:10.1021/es503981n

Record-Breaking Lake Erie Hypoxia during 2012 Drought

2014· article· en· W2332525138 on OpenAlexaff
Yuntao Zhou, A. M. Michalak, Dmitry Beletsky, Yerubandi R. Rao, R. Peter Richards

Bibliographic record

VenueEnvironmental Science & Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersU.S. Army Corps of EngineersU.S. Geological SurveyNational Oceanic and Atmospheric AdministrationU.S. Environmental Protection AgencyNational Science Foundation
KeywordsHypoxia (environmental)TributaryEnvironmental scienceBloomStructural basinNutrientClimate changeAlgal bloomOceanographyPrecipitationHydrology (agriculture)EcologyGeographyPhytoplanktonBiologyGeologyOxygenMeteorology

Abstract

fetched live from OpenAlex

Hypoxia has been observed in the central basin of Lake Erie for decades. To understand the impact of various controlling factors, we analyze a record of hypoxic extents for Lake Erie for 1985–2012 and develop a parsimonious model of their interannual variability. We find that the 2012 North American drought and accompanying low tributary discharge was associated with a record-breaking hypoxic event in Lake Erie, whereas a record-setting harmful algal bloom in 2011 was likely associated with only mild hypoxia. River discharge and the timing of nutrient input therefore impact western basin bloom growth and central basin oxygen demand in distinct ways that merit further investigation. Overall, April to June tributary discharge, May to July soluble reactive phosphorus loading, July wind stress, and June northwesterly wind duration explain 82% of the interannual variability of hypoxia, and discharge alone explains 39%, indicating that meteorological factors need to be considered in the development of nutrient management strategies, especially as both extreme precipitation events and droughts become more frequent under a changing climate.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.003
GPT teacher head0.185
Teacher spread0.181 · 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 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

Citations85
Published2014
Admission routes1
Has abstractyes

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