Controls of Lithium and Strontium Isotopes Spatial Variability Across the Yukon River Basin: Implications for Weathering in a Warming Basin
Bibliographic record
Abstract
The Yukon River drains a large catchment underlain by a range of geological units characterized by diverse topography, environmental conditions, and land cover. The Yukon River catchment also displays a permafrost cover gradient and has active alpine glaciers in its headwaters. With ongoing warming, permafrost degradation, glaciers retreat, and land cover changes, the hydrochemistry of the Yukon River will likely change with risks for water quality and ecosystem sustainability. Here, we report elemental and isotopic data across the Yukon River to unravel the processes controlling the geochemistry of the dissolved load across the watershed and investigate the sensitivity of hydrochemistry to climate conditions. We analyze 102 samples from most major tributaries of the Yukon River for major and trace elements, strontium isotope ratio (⁸⁷Sr/⁸⁶Sr) and lithium isotope composition (δ⁷Li). Elemental and isotopic geochemistry displays strong spatial patterns that primarily correlate with lithological, topographic and climatic characteristics over the watershed. In the glacial and mountainous headwaters, we observe low Li/Na ratio and low δ⁷Li values (<15‰). We suggest that glaciated headwaters are characterized by a very high denudation rate and low secondary mineral formation rate explaining the low δ⁷Li values. In most of the permafrost-covered lowlands, we observe relatively constant Li/Na ratio and δ⁷Li values suggesting that weathering intensity is at a steady state. Conversely, in the non-permafrost-covered lowlands, we observe higher and more variable Li/Na ratio and δ⁷Li values suggesting increased weathering intensity when permafrost is absent. In the lowlands, glaciated or not, ⁸⁷Sr/⁸⁶Sr ratios remain within a tight range of values (0.709-0.715) and reflect the variable contribution of carbonate and silicate units throughout the basin. We found an interesting relationship between δ⁷Li values and ⁸⁷Sr/⁸⁶Sr ratios, suggesting a strong geology influence on weathering processes. When ⁸⁷Sr/⁸⁶Sr are low or very high, δ⁷Li values tend to be high (>25‰), whereas when ⁸⁷Sr/⁸⁶Sr are intermediate δ⁷Li values are low. We argue that the presence of igneous rock units within the catchment is an important control of weathering intensity as igneous rock units can contribute fresh primary minerals for weathering reactions. Weathering intensity is also strongly associated with temperature and permafrost cover across the Yukon River. As temperature increases and permafrost thaws, the weathering intensity increases, modifying the flux of elements to rivers. As the climate continues to warm in the future, the Yukon River hydrochemistry will continue to evolve, reflecting the increased contribution of soils and clays to river water chemistry.
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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 | 0.000 | 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".