Calibration and Uncertainty Analysis Using the Sparrow Model for Dissolved‐Solids Transport in the Upper Colorado River Basin
Bibliographic record
Abstract
Abstract Salinity in the Upper Colorado River Basin ( UCRB ) is due to both natural sources and processes, and anthropogenic activities. Given economic damage due to salinity of $295 million in 2010, understanding salinity sources and production together with transport are of great importance. SPA tially Referenced Regressions On Watershed ( SPARROW ) is a nonlinear regression water quality model that simulates sources and transport of contaminants such as dissolved‐solids. However, SPARROW simulations of dissolved‐solids in the UCRB only represent conditions through 1998 due to limited data availability. More importantly, prior simulations focused on a single year calibration and its transferability to other years, and the validity of this approach is questionable, given the changing hydrologic and climatic conditions. This study presents different calibration approaches to assess the best approach for reducing model uncertainty. This study conducted simulations from 1999 to 2011, and the results showed good model accuracy. However, the number of monitoring stations decreased significantly in recent years resulting in higher model uncertainty. The uncertainty analysis was conducted using SPARROW results and bootstrapping. The results suggest that the watershed rankings based on salinity yields changed due to the uncertainty analysis and therefore, uncertainty consideration should be an important part of the management strategy.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".