Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project
Bibliographic record
Abstract
Abstract. In the scope of the AQMEII Phase 1 project the GEM-AQ model was run over Europe for the year 2006. The modelling domain was defined using a global variable resolution grid with a rotated equator and uniform resolution of 0.2° × 0.2° over the European continent. Spatial distribution and temporal variability of the GEM-AQ model results were analysed for surface ozone and PM10 concentrations. Model results were compared with measurements available in the ENSEMBLE database. Statistical measures were used to evaluate performance of the GEM-AQ model. The mean bias error, the mean absolute gross error and the Pearson correlation coefficient were calculated for the maximum 8 h running average ozone concentrations and daily mean PM10 concentrations. The GEM-AQ model performance was characterized for station types, European climatic regions and seasons. The best performance for ozone was obtained at suburban stations, and the worst performance was obtained for rural stations where the model tends to underestimate. The best results for PM10 were calculated for urban stations, while over most of Europe concentrations at rural sites were too high. Discrepancies between modelled and observed concentrations were discussed in the context of emission data uncertainty as well as the impact of large-scale dynamics and circulation of air masses. Presented analyses suggest that interpretation of modelling results is enhanced when regional climate characteristics are taken into consideration.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".