NWA Electronic Journal of Operational Meteorology
Bibliographic record
Abstract
This paper examines the use of backward-in-time Lagrangian trajectory simulations to explore source regions associated with atmospheric tracer substances. A non-diffusive trajectory model consisting of a spatially varied ensemble of 30 members (BAM-30) is compared to a Lagrangian stochastic model (BLSM) using 50,000 particles. The goal is to explore whether the simpler model can provide useful source location information. Three cases were chosen for examination, all of which occurred during a significant rainfall event during June 2005 over southern Alberta, Canada. For both models, particles were released at 0000 UTC on the selected days, within a cylinder having a height of 3000 m and radius of 100 km centered on Lethbridge, Alberta. Particles were then back-tracked for 336 hr. Results show that, even though particles became separated by very large distances (on the order of thousands of km) after sufficient time had elapsed, the BAM-30 particles were generally contained within the main “cloud ” of BLSM particles. This suggests the BAM-30 method could provide useful source information. We conclude that, for large source regions, such as vapor sources, a
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.295 | 0.271 |
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".