Open science in practice: Learning integrated modeling of coupled surface‐subsurface flow processes from scratch
Bibliographic record
Abstract
Abstract Integrated modeling of coupled surface‐subsurface flow and ensuing role in diverse Earth system processes is of current research interest to characterize nonlinear rainfall‐runoff response and also to understand land surface energy balances, biogeochemical processes, geomorphological dynamics, etc. A growing number of complex models have been developed for water‐related research, and many of these are made available to the Earth science community. However, relatively few resources have been made accessible to the potentially large group of Earth science and engineering users. New users have to invest an extraordinary effort to study the models. To provide a stimulating experience focusing on the learning curve of integrated modeling of coupled surface‐subsurface flow, we describe use cases of an open source model, the Penn State Integrated Hydrologic Model, PIHM. New users were guided through data processing and model application by reproducing a numerical benchmark problem and a real‐world watershed simulation. Specifically, we document the PIHM application and its computational workflow to enable intuitive understanding of coupled surface‐subsurface flow processes. In addition, we describe the user experience as important evidence of the significance of reusability. The interaction shows that documentation of data, software, and computational workflow in research papers is a promising method to foster open scientific collaboration and reuse. This study demonstrates how open science practice in research papers would promote the utility of open source software. Addressing such open science practice in publications would promote the utility of journal papers. Further, popularization of such practice will require coordination among research communities, funding agencies, and journals.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".