Cryosphere and lithology influence the hydrological gradients of high elevation Alpine catchments
Bibliographic record
Abstract
In high-elevation systems influenced by receding cryosphere, geomorphology and lithology can strongly influence the hydrology of river networks. During summer 2022–2023, we studied the water temperature, δ 18 O, pH, major ions, and trace element concentrations at two headwater catchments in the Eastern Italian Alps. We investigated the main streams at the spring and below the confluences with tributaries from glaciers, intact and relict rock glaciers, young moraines, and till deposits. In the non-glacierized catchment (6.3 km 2 ), water temperature increased from 1.6 °C at the intact rock glacier spring to 7.3 ± 1.5 °C at the catchment outlet, despite the inputs from till and rock glacier springs with <3.0 °C waters. In the glacierized catchment (3.7 km 2 ), the proglacial reaches had a water temperature of 6.9 ± 2.6 °C and the inputs from cold rock glacier springs decreased the water temperatures by 2–4 °C along the stream. Due to predisposing lithology, at the glacierized catchment the concentrations of trace elements such as Ni, Al, Mn, Zn, Y, and Li were high along the entire river network except in till and the relict rock glacier springs, which are not influenced by the cryosphere. For both catchment outlets, end-member mixing models estimated 60–65 % contribution from rock glaciers to stream runoff. In both river systems, meltwater from snow and ice was the dominant runoff component, with rainwater accounting for 20–30 % of runoff in the non-glacierized catchment and for <10 % in the glacierized one.
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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 | 0.001 |
| 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 | 0.001 | 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".