Modelling the impact of the bidirectional exchange of NH3 from the Great Lakes on a regional and local scale using GEM-MACH
Bibliographic record
Abstract
Ammonia emissions from oceans are recognized as one of the most significant natural sources of ammonia globally; however, freshwater sources are rarely considered significant. The Great Lakes region, containing the largest network of freshwater lakes in the world, and a significant urbanized population exceeding 20 million, provides a unique opportunity to evaluate the potential for lacustrine surfaces to contribute to regional ammonia levels. In this work, we combine an analysis of 20 years of water quality data from the Great Lakes region and local water measurements near the Greater Toronto Area with the GEM-MACH (Global Environmental Multiscale model – Modelling Air quality and CHemistry) chemical transport model to examine the influence of the Great Lakes on atmospheric ammonia. This analysis demonstrates that while regional ammonia levels are largely controlled by known terrestrial anthropogenic sources, lacustrine emissions increase summertime (July – September) monthly average NH3 levels by 5 – 8% over the largest regional urban centers, with daily increases of up to 10 – 20%, confirming that the Great Lakes represent a regionally significant natural source of ammonia to the atmosphere.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".