RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"
Bibliographic record
Abstract
Corresponding peer-reviewed publication This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in: Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1. When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Known bugs and limitations in this dataset or the associated manuscript. In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as \(\alpha = 0.3876\) in Figure 5c changes to \(\alpha = 0.4507\) after correction. The resulting value of the ideal inflation factor hence changes from \(I = {1 \over 0.3876} \approx 2.58\) to \(I = {1 \over 0.4507} \approx 2.22\). This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to \(I = 2.58\) than to \(I = 1\) or \(I = 5\), i.e. the three values that were evaluated. Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.288 | 0.249 |
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".