Evidence for influence of mineral weathering on stream water sulphate in Vermont and New Hampshire (USA)
Bibliographic record
Abstract
Abstract Mass balance studies in forested catchments in the northeastern USA show that S losses via streamwater SO 4 2− exceed measured atmospheric S inputs. Possible sources of the excess S loss include underestimated dry deposition, mineralization of organic S in soils, desorption of soil sulphate, oxidation of recently formed sulphides and mineral weathering. Evaluating the relative contribution of these sources and processes to SO 4 2− export is important to our understanding of S cycling as well as to policy makers in their evaluation of the efficacy of S emission controls. In order to evaluate the potential for mineral weathering contributions to SO 4 2− export, we measured concentration and isotopic composition (δ 34 S and δ 18 O) of SO 4 2− in stream water, and concentration and δ 34 S values of four S fractions in bedrock and soil parent material in catchments of varying geological composition. Geological substrates with low S concentrations were represented by catchments underlain by quartzite and granite, whereas geological substrates with high S concentrations were represented by catchments underlain by sulphidic slate, schist and metavolcanic rocks. Catchments with S‐poor bedrock had stream‐water SO 4 2− concentrations <100 µeq L −1 and isotopic values consistent with those of atmospheric SO 4 2− that had been cycled through the organic soil pool. Catchments with S‐rich bedrock had stream‐water SO 4 2− concentrations ranging from 56 to 229 µeq L −1 . Isotopic values deviated from those of SO 4 2− in atmospheric deposition, clearly indicating a mineral weathering source in some cases, whereas in others spatial variability of mineral δ 34 S values precluded the isotopic detection of a weathering contribution. These results, along with evidence suggesting formation of secondary sulphate minerals in bedrock weathering rinds, indicate that mineral weathering may be an important source of S in the surface waters of some forested catchments in the northeastern USA. Copyright © 2004 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".