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Record W2990893428 · doi:10.1002/hyp.13653

Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model

2019· article· en· W2990893428 on OpenAlexaff
Fei Dong, Chenxi Mi, Michael Hupfer, Karl‐Erich Lindenschmidt, Wenqi Peng, Xiaobo Liu, Karsten Rinke

Bibliographic record

VenueHydrological Processes · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersMajor Science and Technology Program for Water Pollution Control and TreatmentChina Institute of Water Resources and Hydropower ResearchBundesministerium für Bildung und ForschungNational Natural Science Foundation of China
KeywordsThermoclineStratification (seeds)Eddy diffusionWind speedThermal diffusivityEnvironmental scienceDiffusionForcing (mathematics)GeologyTurbulenceAtmospheric sciencesHydrology (agriculture)MeteorologyClimatologyPhysicsThermodynamicsOceanography

Abstract

fetched live from OpenAlex

Abstract Vertical turbulent diffusivity (K z ), which can be estimated from water temperature, is a key factor in the evolution of water quality in lentic waters. In this study, we analysed the capability of a three‐dimensional hydrodynamic model (EFDC) to capture water temperature and vertical diffusivity in Lake Arendsee in the Northern German plain. Of particular interest to us is to evaluate the model performance for capturing the diffusion minimum within the metalimnion and analyse the response of the metalimnetic K z to meteorological forcing, namely changing wind speed and warming. The comparison confirmed that the calibrated model could reproduce both stratification dynamics and vertical diffusion profiles in the lake. The model was also shown to be able to capture the duration and vertical extent of the metalimnetic diffusion minimum. The scenario results illustrate that, compared to air temperature, wind velocity appeared to be the more influential meteorological variable on the vertical exchange within the metalimnion. While increasing wind velocities mostly affected the minimum values of K z in the metalimnion and thus led to intensified vertical exchange, the reduction of wind velocity mostly affected the depth of minimal K z , but not its absolute value.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.241
Teacher spread0.214 · 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.

Study designSimulation or modeling
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

Citations21
Published2019
Admission routes1
Has abstractyes

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