Estimation of Continuous Streamflow in Ontario Ungauged Basins: Comparison of Regionalization Methods
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
Regionalization, a process of transferring hydrological information [i.e., parameters of a conceptual rainfall-runoff model, namely, the McMaster University-Hydrologiska Byråns Vattenbalansavdelning (MAC-HBV)] from gauged to ungauged basins, was applied to estimate continuous flows in ungauged basins across Ontario, Canada. To identify appropriate regionalization methods, different regionalization methods were applied, including the spatial proximity [i.e., kriging, inverse distance weighted (IDW), and mean parameters], physical similarity, and regression-based approaches. Furthermore, an approach coupling the spatial-proximity (IDW) method and the physical similarity approach is proposed. The analysis results show that the coupled regionalization approach as well as the IDW and kriging produce better model performances than the remaining three. Further investigations show that the coupled-regionalization approach provides slightly better performances than the other two spatial proximity methods. In addition, a modified Monte Carlo simulation method is used to assess the estimated flow confidence intervals. The prediction confidence intervals provide additional information on the range of variability of the simulated continuous streamflow in the ungauged basins, and this can be particularly useful for decision making, such as environmental flow determination in ungauged basins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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