Loading estimate methods to support integrated watershed-lake modelling: Duffins Creek, Lake Ontario
Bibliographic record
Abstract
Integrated watershed-lake modelling requires high quality data for calibration and validation. The two-phase loading estimate approach presented here provides a more confident estimate of nutrient loads for these models. Phase 1 establishes the initial range of the loading estimates using averaging algorithms, ratio estimators, event mean concentration (EMC) and regression-based methods. For Duffins Creek outlet, the 2007, 2008 and 2009 ranges are 6.2–30, 22.3–78 and 19.5–242 tonnes of total phosphorus (TP), respectively. After combining the Beale ratio estimator and the regression-based methods in Phase 2, the 2007, 2008 and 2009 ranges are reduced to 13–17, 57–73 and 69–92 tonnes TP, respectively. The reduction represents the 0 and 28.07% upper bound bias of the regression-based method. Applying this information to the regression-based methods, daily and monthly ranges with a lower bound with no adjustment and with upper bound as 1.2807 times the regression-based TP load estimates are established. These loads are then used in integrated watershed-lake model calibration and validation to improve the model predictions.
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How this classification was reachedexpand
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 | 0.011 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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; both teacher heads agree on what is shown here.
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".