A surrogate regression approach for computing continuous loads for the tributary nutrient and sediment monitoring program on the Great Lakes
Bibliographic record
Abstract
Water quality (WQ) in many Great Lake tributaries has been degraded (increased nutrient and sediment concentrations) due to changes in their watersheds, resulting in downstream eutrophication. As part of the Great Lakes Water Quality Agreement, specific goals were established for loading of specific constituents (e.g., phosphorus). In 2010, the Great Lakes Restoration Initiative was launched to identify problem areas, accelerate restoration efforts, and track their progress. In 2011, the U.S. Geological Survey established a monitoring program on 30 tributaries to the lakes, representing ~ 46% of the U.S. draining area and the spectrum of land uses. Discrete measurements of nutrients and suspended sediment, and continuous measurements of flow and WQ surrogates (turbidity, temperature, specific conductance, pH, and dissolved oxygen) are being collected in these tributaries to document their WQ and estimate continuous (5-min) loading. To estimate loadings, two regression models were developed for each constituent for each site: one using continuous flow and a seasonality factor; and one using flow, seasonality, and continuous surrogates. Variables included in the final models for each constituent were chosen from the explanatory variables that worked “best” for all sites. In computing loads, when continuous surrogate data were unavailable for short periods, loads were computed using the flow and seasonality models. Prediction intervals for all loads were calculated using results from both models. These results provide a better understanding of short-term variability and long-term changes in loading affecting the environmental health of the Great Lakes than traditional regression techniques that employ only flow and seasonality parameters.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".