What are the top priorities for (co-) creating hydrological forecast systems that add value across spatial scales and time horizons? Outcomes from the 2023 HEPEX workshop
Bibliographic record
Abstract
Creating forecast systems that add value across spatial scales and time horizons is crucial for a variety of fields, from meteorology and climate science to business and public policy. The priorities for developing such systems may vary depending on the specific domain and objectives. The international community of practice of the Hydrological Ensemble Prediction Experiment (HEPEX) has been seeking to advance the science and practice of hydrological ensemble prediction and its use in impact- and risk-based decision-making. Over the years, HEPEX has been promoting knowledge utilising cutting-edge techniques and data to innovate hydrological forecasting methods, products and systems, and improve services for users in the water-related sectors. During the 2023 HEPEX workshop in Norrköping, Sweden, (https://hepex.org.au/hepex-workshop-2023-forecasting-across-spatial-scales-and-time-horizons/), the community proposed and elaborated on the key priorities for (co-)creating hydrological forecast systems that are broadly applicable and can add value for local/regional decision-making. The notes from five breakout groups (about 10 participants in each group) were collected and analysed, while the proposed efforts and ways forward were classified and prioritised. Here, we present the outcomes from these breakout discussions, while we support the identified priorities providing backgrounds on the science needed, the HEPEX contribution towards these priorities, and the path forward for contributing to the United Nations Early Warnings for All (EW4All) initiative.
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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 | 0.077 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".