Choosing methods for estimating dissolved and particulate riverine fluxes from monthly sampling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In discrete water quality surveys, riverine fluxes are associated with unknown uncertainties (biases and imprecisions). Annual flux errors have been determined from the generation of discrete surveys by Monte Carlo sorting for monthly sampling, from 10 years of daily records (120 records). Eight calculation methods were tested for suspended particulate matter, dissolved solids and dissolved and total nutrients in medium to large basins (103 to 106 km2) covering a wide range of hydrological conditions and riverine biogeochemistry. The performance of each method was analysed first by type of riverine material, which appeared to be much less pertinent than the flux variability matrix. The latter combines the river flow duration in two percent of time (W2%) and the truncated exponent (b50sup) defining the relationship of concentration vs discharge (C–Q) at higher flows (C = aQb50sup). As flux variability increases (high W2% and/or high b50sup), averaging and rating curve methods become less efficient compared to hydrograph separation methods. Flux biases and imprecisions were plotted in the [W2%, b50sup] matrix for discrete monthly surveys.Editor Z. W. KundzewiczCitation Raymond, S., Moatar, F., Meybeck, M., and Bustillo, V., 2013. Choosing methods for estimating dissolved and particulate riverine fluxes from monthly sampling. Hydrological Sciences Journal, 58 (6), 1326–1339.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it