Data fusion of modelled and-measured deposition in the U.S. and Canada, part I: Description of methodology and validation of wet deposition of sulfur and nitrogen
Bibliographic record
Abstract
This study constitutes Part I of the ADAGIO project (Atmospheric Deposition Analysis Generated by Integrating Observations into model), initiated by Environment and Climate Change Canada (ECCC) to improve the accuracy of sulfur (S), nitrogen (N), and ozone deposition estimates across Canada and the United States. Using deterministic data fusion , it combines numerical models and ground-level observations to generate seasonal objective analyses (OAs) for twelve chemical species, including gases, particulates, and precipitation species, over multiple years (2010, 2013, 2014, 2015, 2016, 2019). OAs are computed seasonally to enable high-resolution estimates of annual total deposition, validate air quality models, assess model errors, and evaluate ecosystem impacts, such as acidification and eutrophication . ADAGIO employs Optimal Interpolation, optimized through sensitivity tests, to integrate measurements with archived outputs from ECCC’s GEM-MACH regional air quality model. The ADAGIO project spans three papers, addressing wet deposition (pollutants in precipitation), dry deposition (gas and particles deposited onto surfaces), and total annual deposition (wet and dry combined). Objectives of ADAGIO include deriving total annual N and S deposition over North America and comparing seasonal OAs with model outputs to identify biases and errors. Part I, presented here, focuses on wet deposition and represents the first application of optimal interpolation for fusing wet deposition estimates with observational data over a continental scale. This innovative approach using OI marks a significant advancement in deposition data fusion methodologies over North America
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".