An example of using open-source data and hydrology models for classroom exercises and term projects
Bibliographic record
Abstract
Recent developments in hydrologic science include a strong focus on open-source data sets and modeling tools. These developments can easily be leveraged into hydrologic education in the form of classroom exercises and term projects. Here we present a computational exercise designed to teach the concept of model structure uncertainty to students, using a specific selection of two catchments and two simple conceptual models from open-source data and tools. The exercise first familiarizes the students with the modeling tool they will use and then has them calibrate and evaluate model performance on each combination of model and catchment. For these specific catchment and models, model structure uncertainty is the dominant source of uncertainty (compared to data, parameter and objective function uncertainties). The exercise includes guiding questions that help the students reach the defined learning goals. Trials at the Technische Universität Dresden show that the exercises are effective in doing so. This introduction to open-source models and data yields the benefit of being easily expanded on during further exercises and term projects.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".