“Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models” by Dale M. Robertson and David A. Saad<sup>2</sup>
Bibliographic record
Abstract
Richards, R. Peter, Ibrahim Alameddine, J. David Allan, David B. Baker, Nathan S. Bosch, Remegio Confesor, Joseph V. DePinto, David M. Dolan, Jeffrey M. Reutter, and Donald Scavia, 2012. Discussion –“Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models” by Dale M. Robertson and David A. Saad. Journal of the American Water Resources Association (JAWRA) 1‐10. DOI: 10.1111/jawr.12006 Abstract: Results from the Upper Midwest Major River Basin (MRB3) SPARROW model and underlying Fluxmaster load estimates were compared with detailed data available in the Lake Erie and Ohio River watersheds. Fluxmaster and SPARROW estimates of tributary loads tend to be biased low for total phosphorus and high for total nitrogen. These and other limitations of the application led to an overestimation of the relative contribution of point sources vs. nonpoint sources of phosphorus to eutrophication conditions in Lake Erie, when compared with direct estimates for data‐rich Ohio tributaries. These limitations include the use of a decade‐old reference point (2002), lack of modeling of dissolved phosphorus, lack of inclusion of inputs from the Canadian Lake Erie watersheds and from Lake Huron, and the choice to summarize results for the entire United States Lake Erie watershed, as opposed to the key Western and Central Basin watersheds that drive Lake Erie’s eutrophication processes. Although the MRB3 SPARROW model helps to meet a critical need by modeling unmonitored watersheds and ranking rivers by their estimated relative contributions, we recommend caution in use of the MRB3 SPARRROW model for Lake Erie management, and argue that the management of agricultural nonpoint sources should continue to be the primary focus for the Western and Central Basins of Lake Erie.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".