The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
Bibliographic record
Abstract
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library that streamlines the creation and analysis of box models in earth sciences. With its modular, object-oriented design, ESBMTK simplifies the study of systems such as the long-term carbon cycle or the impact of atmospheric CO 2 variations on ocean chemistry. By standardizing and clarifying how models are defined, the library enhances code readability and serves as a self-documenting tool, making it approachable for undergraduate students and efficient for researchers. ESBMTK automatically translates user-defined models into equations which are solved using established numerical libraries. It also includes built-in functionality for common tasks such as ocean–atmosphere gas exchange, marine carbonate chemistry, isotope effects, and perturbation scenarios. The library's core interface is stable, supported by comprehensive documentation, and available as open-source software through the pip and conda package management systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.032 |
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".