Community workflows for large-domain hydrologic modeling: Separating model-agnostic and model-specific configuration steps to promote reproducibility, efficiency and transparency
Bibliographic record
Abstract
<!--!introduction!--> Earth System Models are rapidly advancing to run simulations at higher resolutions and to include more and more components relevant for accurate modeling of Earth System processes. This comes with an ever-present need to create new model configurations and thus a burden on users to (re-)process model input data and (re-)do analyses. Such work is typically cumbersome, and often opaque and poorly reproducible. Here we present ongoing work on fostering a community modeling approach in the hydrologic sciences, where model configuration code is freely shared between different modeling groups. The key insight that underpins this work is a realization that our models may differ in details, but generally have similar (if not the same) input requirements. By dividing model configuration procedures into model-agnostic and model-specific steps, we were able to create a code base that consists of a shared data processing core (the model-agnostic part) coupled to thin layers of model-specific code that convert the prepared data into the exact specifications the models need. This setup has reduced model configuration time costs considerably, while also increasing transparency and reproducibility of the resulting model configurations. These principles are widely applicable and essential in current times, where society’s need for Earth System predictions far exceeds the capabilities of individual modeling groups. Building effective collaborations between modeling groups and disciplines are critical to provide these predictions.
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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 | 0.027 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".