Chempath 1.0: an open-source pathway analysis program for photochemical models
Bibliographic record
Abstract
We describe the development of Chempath, an open-source pathway analysis program for photochemical models. This algorithm can help understand the results of complex photochemical models by identifying the most important reaction chains (pathways) for the production and destruction of a species of interest in a reaction system. The algorithm can also quantify the contributions of the pathways to the production and destruction of a species. Chempath is an open-source Python re-implementation of the algorithm developed by Lehmann (2004). However, Chempath does not include the balance of concentration changes and reaction rates that Lehmann's algorithm uses to eliminate imbalances due to numerical errors. Instead, Chempath quantifies the contributions of these imbalances to the production and destruction of a species. We demonstrate how to apply Chempath to both a simple box model and a one-dimensional photochemical model, using a reaction system for Earth's present-day atmosphere. Chempath can identify well-known chemical mechanisms for O 3 production and destruction in these models, suggesting that this algorithm can be applied to understand photochemical models of less-well-known atmospheres, like past and exoplanet atmospheres.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".