Diatom community responses to long‐term multiple stressors at Lake Gusinoye, Siberia
Bibliographic record
Abstract
Global freshwater systems are threatened by multiple anthropogenic stressors via impacts on ecological structure and function necessary to maintain their health. In order to properly manage freshwater ecosystems, we must have a better understanding of the ecological response to human‐induced stressors, especially in multiple stressor environments. When long‐term observational records are scarce or non‐existent, paleolimnology provides a means to understanding ecological response to long‐term stress. Lake Gusinoye is a large, deep lake in continental southeast Siberia, and has been subject to multiple human‐induced stressors since the 19th century. Diatom assemblages since the late 17th century were reconstructed from a Lake Gusinoye sediment core to increase our understanding of the response of primary producer communities to centuries of environmental change. Records of anthropogenic contamination of Lake Gusinoye (as indicated by spheroidal carbonaceous particle, trace metal, and element records) indicate increases in regional and local development c. 1920. Diatom assemblages were initially dominated by Aulacoseira granulata , which declined beginning in the 18th century, likely as a response to hydrological change in the Gusinoye basin due to regional climate warming following the termination of the Little Ice Age ( LIA ). Significant diatom compositional turnover was observed since the 19th century at Lake Gusinoye. Since the early 20th century, Lake Gusinoye diatom assemblages have changed more profoundly as a result of multiple anthropogenic stressors, including nutrient influx, aquaculture, and wastewater discharge from the Gusinoozersk State Regional Power Plant. Recent diatom assemblages are dominated by Lindavia ocellata and nutrient‐rich species, including Fragilaria crotonensis and Asterionella formosa . Evidence of continued nutrient enrichment at Lake Gusinoye is likely due to aquaculture in the lake, and suggests potential interactive effects of warming regional temperatures and increasing nutrients (eutrophication).
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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) | 0.001 | 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".