Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model
Bibliographic record
Abstract
Thermal bar is an important phenomenon in large, temperate lakes like LakeOntario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits theexchange of nutrients. Evolution of the spring thermal bar through Lake Ontario issimulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC).The model is forced with the hourly meteorological data from weather stations around thelake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation isperformed from April to July, 2011; on a 2-km grid. The numerical model has beencalibrated by specifying: appropriate initial temperature and solar radiation attenuationcoefficients. The existing evaporation algorithm in EFDC is updated to modified masstransfer approach to ensure correct simulation of evaporation rate and latent heatflux.Reasonable values for mixing coefficients are specified based on sensitivity analyses. Themodel simulates overall surface temperature profiles well (RMSEs between 1-2°C). Thevertical temperature profiles during the lake mixed phase are captured well (RMSEs <0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. Anupdate of vertical mixing coefficients is under investigation to improve the summer thermalstratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".