A Canadian viewpoint on data, information and uncertainty in the context of prediction in ungauged basins
Bibliographic record
Abstract
The quality (i.e. the degree of uncertainty that results from the interpretation and analysis) of information dictates its value for decision making. There has been much progress towards improving information on the water budgets of ungauged basins by improving knowledge, tools and techniques during the Prediction in Ungauged Basins (PUB) initiative. These improvements, at least in Canada, have come through efforts in both hydrological process and statistical hydrology research. This paper is a review of some recent Canadian PUB efforts to use data to generate information and reduce uncertainty about the hydrological regimes of ungauged basins. The focus is on the Canadian context and the problems it presents, but the lessons learned are applicable to other countries with similar challenges. With a large land mass that is relatively poorly gauged, novel approaches have had to be developed to extract the most information from the available data. It can be difficult in Canada to find gauged or research basins sufficiently similar to ungauged sites of interest that contain the data required to force either statistical or deterministic models. Many statistical studies have improved information or at least an understanding of the quality of that information, of ungauged basin streamflow regimes using innovative regression-based approaches and pooled frequency analysis. Hydrological process research has reduced knowledge uncertainty, particularly in regard to cold regions processes, and this situation has led to the development of new algorithms that are reducing predictive uncertainty. There remains much to do. Current progress has created an opportunity to better integrate statistical and deterministic models via data assimilation of regionalization model estimates and those from coupled atmospheric-hydrological models. Aspects of such a modelling system could also provide more robust uncertainty analyses than traditional approaches.
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.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".