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Record W3100596960 · doi:10.1029/2019wr027040

High Sensitivity of Lake Hypoxia to Air Temperatures, Winds, and Nutrient Loading: Insights From a 3‐D Lake Model

2020· article· en· W3100596960 on OpenAlexafffund
Serghei A. Bocaniov, Kevin G. Lamb, Wentao Liu, Yerubandi R. Rao, Ralph E. Smith

Bibliographic record

VenueWater Resources Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Waterloo
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsHypolimnionHypoxia (environmental)Environmental scienceWind speedForcing (mathematics)NutrientAtmospheric sciencesHydrology (agriculture)OxygenEutrophicationOceanographyEcologyGeologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract A three‐dimensional hydrodynamic‐ecological model is applied to Lake Erie to predict the response of dissolved oxygen (DO) to independent changes in air temperature, wind speeds and total phosphorus (TP) loading. Warmer temperatures and lower wind speeds increased the size and duration of hypoxic and anoxic regions by lengthening the stratified period. Decreased wind speed increased hypolimnion thickness while decreasing its temperature and DO consumption rate. Decreased TP loading improved DO conditions with a reduction of 75% effectively abolishing hypoxia. Anoxia was more sensitive to air temperature, wind, and nutrient changes than was hypoxia. New metrics that capture the spatial and temporal dimensions of low DO conditions were more sensitive than the commonly cited maximum areas of hypoxia or anoxia. Over most of the relevant range of forcing factors, the simple and first‐order effect of a 1°C temperature change was equivalent to a 10–14% change in TP loads, while a 1% change in wind speed was equivalent to a 2–3% change in TP loads. Reduced ice cover in warmer climates will likely increase air temperature effects even further.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.232
Teacher spread0.203 · 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

Citations41
Published2020
Admission routes2
Has abstractyes

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