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 SO42− 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 SO42− 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 SO42− export, we measured concentration and isotopic composition (δ34S and δ18O) of SO42− in stream water, and concentration and δ34S 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 SO42− concentrations <100 µeq L−1 and isotopic values consistent with those of atmospheric SO42− that had been cycled through the organic soil pool. Catchments with S‐rich bedrock had stream‐water SO42− concentrations ranging from 56 to 229 µeq L−1. Isotopic values deviated from those of SO42− in atmospheric deposition, clearly indicating a mineral weathering source in some cases, whereas in others spatial variability of mineral δ34S 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".