Investigating summer thermal stratification in Lake Ontario
Bibliographic record
Abstract
Summer thermal stratification in Lake Ontario is simulated using the 3Dhydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperaturedifferences establish strong vertical density gradients (thermocline) between the epilimnionand hypolimnion. Capturing the stratification and thermocline formation has been achallenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982)vertical mixing scheme, we have implemented an unidimensional vertical model that usesdifferent eddy diffusivity formulations above and below the thermocline (Vincon-Leite,1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological datafrom weather stations around the lake, flow data for Niagara and St. Lawrence rivers; andlake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers followingsigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughlyinvestigated. The model has been calibrated for appropriate solar radiation coefficients andhorizontal mixing coefficients. Overall the new implemented diffusivity algorithm showssome successes in capturing the thermal stratification with RMSE values between 2-3°C.Calibration of vertical mixing coefficients is under investigation to capture the improvedthermal stratification.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".