Decoding long-term trends in the wet deposition of sulfate, nitrate, and ammonium after reducing the perturbation from climate anomalies
Bibliographic record
Abstract
Long-term trends of wet deposition of inorganic ions are affected by multiple factors, among which emission changes and climate conditions are dominant ones. To assess the effectiveness of emission reductions on the wet deposition of pollutants of interest, contributions from these factors to the long-term trends of wet deposition must be isolated. For this purpose, a two-step approach for preprocessing wet deposition data is presented herein. This new approach aims to reduce the impact of climate anomalies on the trend analysis so that the impact of emission reductions on the wet deposition can be revealed. This approach is applied to a 2-decade wet deposition dataset of sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+) at rural Canadian sites. Analysis results show that the approach allows for statistically identifying inflection points on decreasing trends in the wet deposition fluxes of SO42- and NO3- in northern Ontario and Quebec. The inflection points match well with the three-phase mitigation of SO 2 emissions and two-phase mitigation of NO x emissions in Ontario. Improved correlations between the wet deposition of ions and their precursors' emissions were obtained after reducing the impact from climate anomalies. Furthermore, decadal climate anomalies were identified as dominating the decreasing trends in the wet deposition fluxes of SO42- and NO3- at a western coastal site. Long-term variations in NH4+ wet deposition showed no clear trends due to the compensating effects between NH 3 emissions, climate anomalies, and chemistry associated with the emission changes of sulfur and nitrogen.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".