Effects of advancing treelines and melting glaciers on alpine lake ecosystems: a mesocosm experiment
Bibliographic record
Abstract
Climate warming promotes the upward advance of mountain treelines, thereby increasing allochthonous inputs of terrestrial organic matter (OM) into alpine lakes. Higher temperatures also accelerate glacial ablation, altering inputs of finely eroded rock particles, termed “glacial flour”. OM and glacial flour (GF) both affect aquatic ecosystems; however, a knowledge gap exists concerning their combined impact. To test for the direct and interactive effects of OM and GF, we conducted a crossed two-factor outdoor mesocosm experiment. We hypothesized that GF sequesters OM through adsorption, thus reducing its effects on the abiotic environment and phytoplankton community. Addition of GF decreased underwater attenuation of ultraviolet radiation by the OM amendment (i.e., a GF–OM interaction), but not the pronounced positive effects of OM on nutrients and phytoplankton chlorophyll. GF did mediate the effect of OM on phytoplankton community composition by suppressing diatoms. These findings highlight the potential for future shifts in allochthonous inputs away from GF and towards OM to stimulate high-elevation lake ecosystems as glaciers ablate and treeline vegetation migrates to higher elevations under a warming climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".