Climate factors as a possible trigger of modern ecological changes in shallow zone of Lake Baikal (Russia)
Bibliographic record
Abstract
The world’s freshwater ecosystems, sources of drinking water, are threatened by eutrophication. Many studies show that global warming leads to changes in aquatic ecosystems. Recently, eutrophication signs have been recorded in Lake Baikal, the world’s largest and deepest lake containing 20% of the world’s fresh water reserves. We have analysed long-term changes of the most sensitive natural factors (air and water temperature, wind regime, precipitation and solar radiation) and revealed their potential impact on emergence of unfavourable signs in the shallow zone of Lake Baikal. The main causes of adverse ecological processes are elevated temperatures of air and coastal water, reduced amount of precipitation, weakening of wind flows, and water exchange processes, and as a result, reduced self-purification. The highest number of anomalous climate changes has been recorded in the XXI century. Moreover, the years of the past decade were the most favourable for emergence of adverse processes in the lake (outbreak of rapid growth of algae and aquatic vegetation, rotting of their remains at the bottom and on the shores of the lake, etc.). Climatic factors will continue causing adverse effects in the shallow zone of Lake Baikal.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".