sympl (v. 0.4.0) and climt (v. 0.15.3) – towards a flexible framework for building model hierarchies in Python
Bibliographic record
Abstract
Abstract. sympl (System for Modelling Planets) and climt (Climate Modelling and Diagnostics Toolkit) are an attempt to rethink climate modelling frameworks from the ground up. The aim is to use expressive data structures available in the scientific Python ecosystem along with best practices in software design to allow scientists to easily and reliably combine model components to represent the climate system at a desired level of complexity and to enable users to fully understand what the model is doing. sympl is a framework which formulates the model in terms of a state that gets evolved forward in time or modified within a specific time by well-defined components. sympl's design facilitates building models that are self-documenting, are highly interoperable, and provide fine-grained control over model components and behaviour. sympl components contain all relevant information about the input they expect and output that they provide. Components are designed to be easily interchanged, even when they rely on different units or array configurations. sympl provides basic functions and objects which could be used in any type of Earth system model. climt is an Earth system modelling toolkit that contains scientific components built using sympl base objects. These include both pure Python components and wrapped Fortran libraries. climt provides functionality requiring model-specific assumptions, such as state initialization and grid configuration. climt's programming interface designed to be easy to use and thus appealing to a wide audience. Model building, configuration and execution are performed through a Python script (or Jupyter Notebook), enabling researchers to build an end-to-end Python-based pipeline along with popular Python data analysis and visualization tools.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.027 |
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".