Modeling short‐term variability of <i>α</i>‐hexachlorocyclohexane in Northern Hemispheric air
Bibliographic record
Abstract
The POP version of the Danish Eulerian Hemispheric Model (DEHM‐POP) is a further development of a 3‐D dynamic atmospheric chemistry transport model covering the Northern Hemisphere, which was originally developed to study atmospheric transport of conventional air pollutants and other atmospheric constituents (e.g., SOX, heavy metals, and CO2). Four different surface compartments (soil, ocean water, vegetation, and snow) are introduced in DEHM‐POP with each compartment including the most dominant dynamic processes determining the exchange between air and the surface type to account for the consecutive cycles of deposition and reemission of persistent organic pollutants (POPs). This model setup makes it possible to study short‐term atmospheric variability of POPs, which is exemplified in this paper by a study of the atmospheric variability of α‐hexachlorocyclohexane (α‐HCH), the major component of the worldwide most used insecticide: technical HCH. Simulated α‐HCH air concentrations are evaluated against measurements from 21 monitoring stations within the model domain, and the model is able to predict the annual average concentration as well as the long‐term trend for the 1990s. Significant correlations between simulated and measured short‐term atmospheric concentrations of α‐HCH are also found at the majority of the investigated monitoring stations, which shows that it is possible to resolve the atmospheric variability of POPs using an atmospheric chemistry transport model. Differences between simulated and measured atmospheric α‐HCH variability can arise because the measurements may be influenced by local features that are not accounted for in the model with the relatively coarse horizontal resolution and surface description.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".