Improving Lake Winnipeg Integrated Environment Modelling with OpenMI
Bibliographic record
Abstract
We have applied OpenMI standards for two lake models applied to Lake Winnipeg to improve interactions among models and model calibration. These two models are OneLay and PolTra, which combine to form a 2-D horizontal, vertically mixed lake model. The source codes for the two models were modified for OpenMI to enable data exchange at each time step. Our approach is to calibrate drag coefficient and bottom coefficients in the OneLay model and settling coefficient, resuspension critical shear velocity and resuspension coefficients in the PolTra model for total suspended sediment. Model calibration statistic RMSE is used to measure the effectiveness of the calibration and is updated on an on-going basis as the simulation runs. This allows for the model simulations to be stopped partway through when the statistics are not improving, which greatly reduces the calibration time. A modelling database is developed to store key results of the multiple simulation runs from the calibration process. A decision support system with an implementation of a genetic algorithm is being implemented to demonstrate the system can be further automated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".