Improving the science and practice of hydrological modelling
Bibliographic record
Abstract
Many hydrological modelling groups face similar challenges, with untapped opportunities to share code and concepts across different model development groups. An active community of practice is emerging, where the focus is not so much on developing a community hydrological model, and more on advancing the science and practice of community hydrological modelling. This presentation will summarize our recent efforts to develop open-source models, methods, and datasets to enable process-based hydrological prediction across large geographical domains. This presentation summarizes our recent efforts to advance the science and practice of hydrological modelling, focusing on recent work to (1) develop multi-source probabilistic hydrometeorological forcing datasets on continental and global domains; (2) advance a flexible approach to represent a myriad of physical processes in a unified modelling framework; (3) improve the numerical robustness and efficiency of large-domain terrestrial system model simulations; and (4) develop extensible and reproducible modeling workflows. The presentation will highlight major scientific challenges, future research needs, and some key opportunities for community collaboration.
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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 | 0.055 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".