Tropospheric transport climate partitioned by surface origin and transit time
Bibliographic record
Abstract
We perform the first analysis of tropospheric transport using the global boundary propagator Green function, ��, which partitions air at every point and time according to both the transit time since last surface contact and the location of that contact. We compute �� for a 3‐year period with the MATCH model driven by NCEP reanalyses. Last contact time is resolved in 3‐d intervals, and last‐contact location is resolved with a global tiling of 41 patches concentrated in the Northern Hemisphere. The transport climate is quantified for four midlatitude receptor regions in terms of the seasonal mean surface‐origin and transit‐time partitioning of the column burden, the surface flux of newly arriving air, and the distribution of air mass in transit from source to receptor surface. At long transit times a nearly receptor‐independent pattern of last‐contact location is governed by where air is injected into the upper troposphere by deep convection and the high terrain of Tibet. The receptor origin composition of the column burden changes only slowly after ∼40 d for winter and fall, while the composition of the flux onto the receptor continues to change at ∼60 d. European and SE Asian air contribute comparably to the flux onto eastern North America, in spite of SE Asian air having the dominant burden. The flux of European air onto SE Asia in winter and fall is larger than the flux of SE Asian air onto Europe. The surface‐to‐surface transport mass distribution, ℛ, is used to identify transit‐time‐dependent source‐receptor teleconnections.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".