Using inverse modeling to estimate parameter values for three dimensional transport of contaminants in Lake Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The Great Lakes form an important freshwater drinking source for many urban areas surrounding the Lakes but also provide a sink for pollutants and runoff. Consequently introducing new drinking water intakes into any of these water bodies requires investigation into local pollutant sources and their transport in order to determine the most appropriate location and depth of any new intake. Two methods involving the calibration of a 3D wind driven transport model, to spill data collected over a 4 week period, are described. The methods include the traditional trial and error approach and the application of a nonlinear inverse model to optimize parameter estimates. Results show that calibration using the inverse modeling approach was an improvement over the traditional trial and error approach by providing a clear quantitative analysis of parameter sensitivity and importance, and ultimately yielding a better fit between observed and simulated data. The calibrated three-dimensional model was ultimately applied to assess the impacts of a potential local pollutant source to several proposed new drinking water intakes located along the north shore of Lake Ontario.</p>
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it