Impact of climate change and industrialization on remote Lake Bolshoe Toko, Siberia 
Bibliographic record
Abstract
To test if recent climate change and pollution affected remote lake ecosystems without direct human influence, we used paleolimnological methods on lake sediments from a large, prestine, and deep lake in Yakutia, Russia. We compared diatoms and sediment-geochemistry from before and after the onset of industrialization in the mid-nineteenth century, at water depths between 12.1 and 68.3 m in Lake Bolshoe Toko. We analyzed diatom species changes and geochemical changes including mercury concentrations. Chronologies were established using 210Pb and 137Cs revealing sedimentation rates between 0.018 and 0.033 cm y-1 at shallow- and deep-water sites, respectively. Increase in light planktonic diatoms (Cyclotella) and decrease in heavily silicified euplanktonic Aulacoseira through time at deep-water sites can be related to warming air temperatures and shorter periods of lake-ice cover, causing pronounced thermal stratification. Diatom beta diversity changed only significantly in shallow-water communities which can be related to the development of new habitats with macrophyte growth. Mercury concentrations increased by a factor of 1.6 as a result of atmospheric fallout. Increases in the chrysophyte Mallomonas indicates a trend towards acidification. We conclude that also remote boreal lakes are susceptible to human-induced long-distance pollution and recent climate change.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".