Long-term Trends in Organic Carbon Concentrations within Cloud Water and Precipitation Samples in the Northeastern United States 
Bibliographic record
Abstract
Recent research at Whiteface Mountain, one of the few remaining sites in the U.S. where long-term cloud water chemistry research has continued to the present day, has revealed a doubling in cloud water organic carbon concentrations since measurements began in 2009. This dramatic increasing trend was an unexpected result, which requires further investigation. The present study attempts to verify these results using additional independent datasets from within the region and explores potential driving factors behind the observed organic carbon trends. Through evaluation of measurements from four additional sites in the north eastern U.S., each with long-term measurements of organic carbon concentrations within bulk cloud water or wet deposition samples, we show that there is strong evidence for a regional increasing trend in organic concentrations within aqueous atmospheric samples. These results provide further context behind the growing inorganic ion imbalance observed in wet deposition samples collected across the eastern U.S. and Canada, as identified in a separate study published in 2021. We discuss hypotheses for the potential driving factors behind the increasing organic carbon trends observed, including increased biomass burning influence, increased biogenic emissions and a changing chemical regime characterized by relatively high concentrations of reactive nitrogen chemical species.
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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 | 0.000 | 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) | 0.000 | 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 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".