Melting Alpine Glaciers Enrich High-Elevation Lakes with Reactive Nitrogen
Bibliographic record
Abstract
Alpine glaciers have receded substantially over the last century in many regions of the world. Resulting changes in glacial runoff not only affect the hydrological cycle, but can also alter the physical (i.e., turbidity from glacial flour) and biogeochemical properties of downstream ecosystems. Here we compare nutrient concentrations, transparency gradients, algal biomass, and fossil diatom species richness in two sets of high-elevation lakes: those fed by snowpack melt alone (SF lakes) and those fed by both glacial and snowpack meltwaters (GSF lakes). We found that nitrate (NO(3)(-)) concentrations in the GSF lakes were 1-2 orders of magnitude higher than in SF lakes. Although nitrogen (N) limitation is common in alpine lakes, algal biomass was lower in highly N-enriched GSF lakes than in the N-poor SF lakes. Contrary to expectations, GSF lakes were more transparent than SF lakes to ultraviolet and equally transparent to photosynthetically active radiation. Sediment diatom assemblages had lower taxonomic richness in the GSF lakes, a feature that has persisted over the last century. Our results demonstrate that the presence of glaciers on alpine watersheds more strongly influences NO(3)(-)concentrations in high-elevation lake ecosystems than any other geomorphic or biogeographic characteristic.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".