Evaluating sulfur dynamics during storm events for three watersheds in the northeastern USA: a combined hydrological, chemical and isotopic approach
Bibliographic record
Abstract
Abstract Concerns related to climate change have resulted in an increasing interest in the importance of hydrological events such as droughts in affecting biogeochemical responses of watersheds. The effects of an unusually dry summer in 2002 had a marked impact on the biogeochemistry of three watersheds in the north‐eastern USA. Chemical, isotopic and hydrological responses with particular emphasis on S dynamics were evaluated for Archer Creek (New York), Sleepers River (Vermont) and Cone Pond (New Hampshire) watersheds. From 1 August to 14 September 2002, all three watersheds had very low precipitation (48 to 69 mm) resulting in either very low or no discharge (mean 0·015, 0·15 and 0·000 mm day −1 for Archer Creek, Sleepers River and Cone Pond, respectively). From 15 September to 31 October 2002, there was a substantial increase in precipitation totals (212, 246 and 198 mm, respectively) with increased discharge. Archer Creek was characterized by a large range of SO 4 2− concentrations (152 to 389 µeq L −1 , mean = 273 µeq L −1 ) and also exhibited the greatest range in δ 34 S values of SO 4 2− (−1·4 to 8·8 ‰ ). Sleepers River's SO 4 2− concentrations ranged from 136 to 243 µeq L −1 (mean = 167 µeq L −1 ) and δ 34 S values of SO 4 2− ranged from 4·0 to 9·0 ‰ . Cone Pond's SO 4 2− concentrations (126–187 µeq L −1 , mean = 154 µeq L −1 ) and δ 34 S values (2·4 to 4·3 ‰ ) had the smallest ranges of the three watersheds. The range and mean of δ 18 O‐SO 4 2− values for Archer Creek and Cone Pond were similar (3·0 to 8·9 ‰ , mean = 4·5 ‰ ; 3·9 to 6·3 ‰ , mean = 4·9 ‰ ; respectively) while δ 18 O‐SO 4 2− values for Sleepers River covered a larger range with a lower mean (1·2 to 10·0 ‰ , mean = 2·5). The difference in Sleepers River chemical and isotopic responses was attributed to weathering reactions contributing SO 4 2− . For Archer Creek wetland areas containing previously reduced S compounds that were reoxidized to SO 4 2− probably provided a substantial source of S. Cone Pond had limited internal S sources and less chemical or isotopic response to storms. Differences among the three watersheds in S biogeochemical responses during these storm events were attributed to differences in S mineral weathering contributions, hydrological pathways and landscape features. Further evaluations of differences and similarities in biogeochemical and hydrological responses among watersheds are needed to predict the impacts of climate change. Copyright © 2008 John Wiley & Sons, Ltd.
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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".