Increasing Nickel Concentrations in a Large River Network of South Tyrol, Eastern European Alps
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Climate change and cryosphere degradation may enhance the concentrations of heavy metals in high-mountain rivers. However, the downstream export of these contaminants to lower elevations is still overlooked. In this study, we investigated the spatial and temporal patterns of dissolved and bioavailable nickel concentrations in the upper Etsch/Adige river basin (1590 km 2; 54 sites) during the period of 2005–2023. Furthermore, we investigated the same concentrations seasonally (2022–2023) along a tributary (Schnals/Senales River), from the glacier origin down to the confluence with the Etsch River (13 sites). Concentrations of both nickel forms increased during the past decade by up to 4 times, yet only in river reaches draining the acidic metamorphic Ötztal Unit. Sulfide oxidation, more intense at sites featuring larger glaciers, rock glaciers, and permafrost extent in their catchment, enhanced nickel concentrations. Along the Schnals River, values were elevated in the proglacial waters (dissolved fraction up to 112 μg L –1 ), gradually decreased moving to lower elevations, and dropped (from 20 to 30 to 2–5 μg L –1 ) downstream of a large reservoir. Currently, bioavailable nickel concentrations exceed the EU environmental quality standards at 40% of the investigated sites, demonstrating sharp environmental implications that may be extended to other similar geological and cryospheric settings.
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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".