Origin detection tools for atmospheric species: FLEXPART-WRF post-processing scripts for the Composite Ratio Method
Bibliographic record
Abstract
Post-processing python scripts for FLEXPART-WRF outputs when applying the Ratio Method (Da Silva et al., in prep., How to trace the origins of short-lived atmospheric species in the Arctic), and configuration file for running FLEXPART-WRF. Readme.txt describes how to use the scripts (FPES_param.py FPES_read.py FPES_analysis.py FPES_plot.py).flexwrf.input is the parameters file for the FLEXPART-WRF runs. The directory contains everything needed (WRF and FLEXPART-WRF outputs, and concentrations series files) to run a test case on 32 days of simulated measurements of an air tracer emitted in open ocean regions. The station is Alert (Canada), and the case is a cropped version of the analysis presented in the paper. NB: The observational data are simulated tracer concentrations time series, produced with WRF-Chem.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.201 | 0.141 |
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".