Operational and Probabilistic Evaluation of AQMEII-4 Regional Scale Ozone Dry Deposition. Time to Harmonise Our LULC Masks
Bibliographic record
Abstract
Abstract. We present the collective evaluation of the regional scale models that took part in the fourth edition of the Air Quality Model Evaluation International Initiative (AQMEII). The activity consists of the evaluation and intercomparison of regional scale air quality models run over North American (NA) and European (EU) domains in 2016 (NA) and 2010 (EU). The focus of the paper is ozone deposition. The collective consists in an operational evaluation (Dennis et al., 2010, namely a direct comparison of model-simulated predictions with monitoring data aiming at assessing model performance. Following the AQMEII protocol and Dennis et al. (2010), we also perform a probabilistic evaluation in the form of ensemble analyses and an introductory diagnostic evaluation. The latter, analyses the role of dry deposition in comparison with dynamic and radiative processes and land-use/land-cover types (LULC), in determining surface ozone variability. Important differences are found across deposition results when the same LULC is considered. Models use very different LULC masks, thus introducing an additional level of diversity in the model results. The study stresses that, as for other kinds of prior and problem-defining information (emissions, topography or land-water masks), the choice of a LULC mask should not be at modeller’s discretion. Furthermore, LULC should be considered as variable to be evaluated in any future model intercomparison, unless set as common input information. The differences in LULC selection can have a substantial impact on model results, making the task of evaluating deposition modules across different regional-scale models very difficult.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".