On the Construction, Comparison, and Variability of Airsheds for Interpreting Semivolatile Organic Compounds in Passively Sampled Air
Bibliographic record
Abstract
Air mass origin as determined by back trajectories often aids in explaining some of the short-term variability in the atmospheric concentrations of semivolatile organic contaminants. Airsheds, constructed by amalgamating large numbers of back trajectories, capture average air mass origins over longer time periods and thus have found use in interpreting air concentrations obtained by passive air samplers. To explore some of their key characteristics, airsheds for 54 locations on Earth were constructed and compared for roundness, seasonality, and interannual variability. To avoid the so-called "pole problem" and to simplify the calculation of roundness, a "geodesic grid" was used to bin the back-trajectory end points. Departures from roundness were seen to occur at all latitudes and to correlate significantly with local slope but no strong relationship between latitude and roundness was revealed. Seasonality and interannual variability vary widely enough to imply that static models of transport are not sufficient to describe the proximity of an area to potential sources of contaminants. For interpreting an air measurement an airshed should be generated specifically for the deployment time of the sampler, especially when investigating long-term trends. Samples taken in a single season may not represent the average annual atmosphere, and samples taken in linear, as opposed to round, airsheds may not represent the average atmosphere in the area. Simple methods are proposed to ascertain the significance of an airshed or individual cell. It is recommended that when establishing potential contaminant source regions only end points with departure heights of less than ∼700 m be considered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".