GEM/POPs: a global 3-D dynamic model for semi-volatile persistent organic pollutants ─ Part 1: Model description and evaluations of air concentrations
Bibliographic record
Abstract
GEM/POPs was developed to simulate the transport, deposition and partitioning of semi-volatile persistent organic pollutants (POPs) in the atmosphere within the framework of Canadian weather forecasting model GEM. In addition to the general processes such as anthropogenic emissions, atmosphere/water and atmosphere/soil exchanges, GEM/POPs incorporates a dynamic aerosol module to provide the aerosol surface areas for the semi-volatile POPs to partition between gaseous and particle phases and a mechanism for particle-bound POPs to be removed. Simulation results of three PCBs (28, 153 and 180) for the year 2000 indicate that the model captured the main features of global atmospheric PCBs when compared with observations from EMEP, IADN and Alert stations. The annual averaged concentrations and the fractionation of the three PCBs as a function of latitudes agreed reasonably well with observations. The impacts of atmospheric aerosols on the transports and partitioning of the three PCBs are reasonably simulated. The ratio of particulate to gaseous PCBs in the atmospheric column ranges from less than 0.1 for PCB28 to as high as 100 for PCB180, increasing from the warm lower latitudes to the cold high latitudes. Application of GEM/POPs in a study of the global transports and budgets of various PCBs accompanies this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".