Uncertainty of nitrate‐N load computations for agricultural watersheds
Bibliographic record
Abstract
Water quality samples for most streams are collected at variable frequencies within monitoring periods of different lengths. On the basis of discrete concentration data obtained from these monitoring studies, loads of various pollutants passing through a gaging station for selected periods may be calculated using various load estimation methods. In this paper, nitrate‐N load estimates were compared with their “true” values, calculated using 6 years of daily nitrate‐N concentration and average discharge data at an agricultural watershed in central Illinois. A Monte Carlo subsampling study was conducted to simulate different sampling scenarios for variable sampling frequencies and different monitoring durations. Load calculations were compared for each sampling scenario based on rating curve, ratio estimator, and flow‐weighted average estimator. In addition, two bias correction techniques (minimum variance unbiased estimator (MVUE) and smearing estimator) were applied to the rating curve method. The monitoring durations were 1, 2, 3, and 6 years, and the sampling frequencies ranged from weekly to bimonthly. The results demonstrated that a desired accuracy of the estimates could be achieved either by sampling more frequently or by monitoring the site longer. Although the ratio and the flow‐weighted average estimators had a small negative bias, in most cases rating curve estimators were positively biased when applied to the study site. Also, neither of the two bias correction techniques, MVUE and smearing estimator, decreased this positive bias. On the contrary, those techniques produced a higher bias, which resulted in increased root‐mean‐square error (RMSE). The rating curve uncorrected for bias, the simple ratio, and the flow‐weighted estimator had a significantly smaller RMSE for all sampling frequencies and all periods of record than the bias‐corrected rating curve methods.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".