eWaterCycle II: an online environment for explorative computational hydrology
Bibliographic record
Abstract
Whether you study compound hazards that require models from different fields of geoscience, or you just want to compare the river discharge predictions from your model to the predictions from another research groups model: running each others (hydrological) models is often a painstaking process. Recognizing the need for hydrologist to not only have access to the software code of each others models, but also to be able to run these models without the tech-support of the researcher that made the model, we have build the eWaterCycle II platform The goals for the eWaterCycle II project is to provide the hydrological community with tools that: Allow the use of a wide variety of models, written in different programming languages, without having to learn those languages. Run models needing large amounts of memory and CPUs. Have access to all the relevant datasets from the community (forcing, observations) Allow advanced use cases such as data assimilation and model coupling studies. Allow the sharing of models with the entire community, both for citing (DOIs) and re-use. Ultimately providing hydrologists with a toolset that allows them to run each other models, but also adept, couple, and in general tinker with models without the headache of having to delve into each others detailed code. Currently one year into this three year project, at the General Assembly we will demonstrate, and make accessible to fellow hydrologist the first version of our system where scientists can: Get started with modelling without installing a single piece of software. Run any of the available models within minutes. Add their own model with minimal effort. Compare output of their model, as well as that of colleagues to standard observations like discharge from the Global River Data Centre. Develop code quickly in a notebook environment. We will demonstrate (and make available to the community) the system we have built during the presentation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.226 | 0.086 |
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".