Bibliographic record
Abstract
[1] The role of low-level transpacific paths in bringing east Asian air to the North American west coast surface is determined using conditional transit-time distributions. These distributions are computed with the MATCH transport model driven by NCEP reanalysis data. Transpacific paths that lie entirely below ∼2.2 km contribute negligibly to the west coast column burden of east Asian air. However, during summer such paths account for about half of the climatological west coast surface mixing ratio of east Asian air with transit times of less than ∼3 weeks. Summertime transport is characterized by distinct upper and lower level transport modes, which form by convective injection and boundary layer outflow into a summertime marine troposphere of high stability and weak baroclinicity. During the other seasons the climatological east Asian air plume organizes into distinct midlatitude and subtropical parts, particularly evident during spring and fall. The midlatitude plume is characterized by mixing along sloping isentropes, which transports low-level air aloft over the western Pacific and brings midtropospheric air to the surface over the eastern Pacific. The subtropical part of the plume is formed by convectively injected air. Lagged, weighted composites show that summertime surface events are characterized by strong low-level Asian outflow, while for the other seasons surface events are associated with enhanced mixing along sloping isentropes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".