Bibliographic record
Abstract
Box modeling is a versatile tool to explore earth systems processes, ranging from transient changes in the marine carbonate system to the long-term evolution of biogeochemical cycles. The Earth Science Box Modeling Toolkit is a python based toolkit that allows for the rapid creation and deployment of box models. It abstracts typical modeling tasks, e.g., air-sea gas exchange, weathering, seafloor carbonate precipitation/dissolution, kinetic isotope fractionation, seawater properties etc., to python classes. Class instances can then simply be combined to build a model. While there is no graphical interface, this approach significantly reduces coding complexity and model development time. The model structure is then parsed and translated into an equation system that is passed to ode solver libraries like ODEPACK. Separating model description from numerical implementation results in well-documented model code, and combines the computational efficiency of stateof-the-art numerical libraries with the ease of use of python. A 12-box model with air-sea gas exchange, tracers for carbon isotopes, and water column carbonate chemistry requires about 1 CPU second to calculate the model evolution over a time period of 10 million years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".