Evaluation of a 3‐D hydrodynamic model and atmospheric forecast forcing using observations in Lake Ontario
Bibliographic record
Abstract
Six‐month observations of surface meteorology, water temperature, and currents in Lake Ontario are used to evaluate a high‐resolution, three‐dimensional hydrodynamic model and the forecasted forcing from a regional version of the Canadian operational global environmental multiscale (GEM) model. The hydrodynamic model is based on the Princeton Ocean Model (POM). Driven by both the observed and modeled surface wind stress and the surface net heat fluxes (SNHF), POM is able to reproduce the observed variations of the lake surface temperature (LST) and vertical stratification conditions at the seasonal and synoptic time scales. The model also has skill in simulating the temporal and vertical variation of currents. The patterns of the simulated horizontal distributions of the LST and lake circulation are consistent with the observed climatology. Model sensitivity experiments reveal that the differences between the simulations using observed and model forcing are mainly due to the difference in wind stress instead of the SNHF. Comparison with meteorological observations suggests that GEM has good accuracy in simulating the SNHF but overestimates the wind. Model sensitivity experiments further revealed that errors in the SNHF have significant impact on simulations of water temperature in the surface and near‐surface layers, whereas errors in wind stress cause significant changes of water temperature in the thermocline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".