Data fusion of modelled and measured deposition in the US and Canada, part II: Dry deposition of sulfur, nitrogen and ozone
Bibliographic record
Abstract
ABSTRACT This study is part of a project which aims to compute the total deposition of sulfur, nitrogen and ozone over both Canada and the US. This initiative is named ADAGIO ( A tmospheric D eposition A nalysis G enerated by I nterpolation of model and O bservations) and is led by Environment and Climate Change Canada. More details about the overall methodology were given in Part I of this project. Similarly, here, a deterministic data fusion methodology is used to combine information from air quality models and surface observations to produce seasonal objective analyses (OAs) for six gas species (O 3 ,SO 2, NO 2 , NO, HNO 3 , NH 3 ) and three dry particulate species (p-NH 4 + , p-NO 3 - , p-SO 4 = ) for multiple years during the period 2010-2019. The main difference in the methodology with PART II is that for dry species, the OAs are obtained for concentration instead of wet deposition measurements. The seasonal dry deposition fluxes and concentrations obtained from air quality model simulations for each species are corrected using the OA concentration fields to obtain the corrected total annual dry deposition. In part II, we focus on the species which contribute the most to the N deposition, that is NO 2, HNO 3 , and NH 3 . ADAGIO yields total dry nitrogen deposition results that are in good agreement with those of the US/TDep project of the US. Comparison with satellite-derived concentrations for NH 3 and NO 2 are also presented and found reasonable. This paper demonstrated that the simple algorithm based on data fusion presented in part I (for wet species) also works for dry species deposition over large territories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".