EASYMORE: A Python package to streamline the remapping of variables for Earth System models
Bibliographic record
Abstract
The Earth System modeling community uses different methods to discretize a landscape in model elements, such as square grids, triangles, or irregular shapes. Mapping data from one spatial configuration to another is an essential part of environmental modeling, and can be time-consuming and cumbersome. In this work, we present a Python package called EASYMORE. EASYMORE stands for EArth SYstem MOdeling REmapper and enables users to quickly and efficiently remap variables, such as precipitation or temperature, from one spatial representation (e.g., unstructured grids) to another (e.g., sub-basins). The package is aimed to increase the efficiency of data preparation for Earth System modeling in a reproducible and transparent manner. The remapped variables, provided in netCDF or CSV formats, can then be used directly or changed to the format needed for intended uses. This manuscript presents examples that show various applications of EASYMORE.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.037 |
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".