The World’s Largest Lakes Water Level Changes in the Context of Global Warming
Bibliographic record
Abstract
The article is focused on the assessment of changes in the average annual water levels of large lakes of the planet in the changing climate conditions characteristic of the recent decades. Eight large lakes, i.e.Baikal, Balkhash, Superior, Issyk-Kul, Ladoga, Onega, Ontario, and Erie, located on the territory of Eurasia and North America, were chosen as the research objects. They were selected because of the availability of a long-term observations series of the water level. As is known, long-term changes in the lakes water level result from variation in the water volume. The latter depends on the ratios between the water balance components of the lake that have developed during a given year, which, in turn, reflect the climatic conditions of the respective years. The features of the water balance structure of the above-mentioned lakes and the intra-annual course of the water level are considered. The available long-term records of observational data on all selected lakes and their stations were divided into two periods: from 1960 to 1979 (the period of stationary climatic situation) and from 1980 to 2008 (the period of non-stationary climatic situation). The homogeneity and significance of trends in the long-term water level series of records have been estimated. It has been established that over the second period the nature and magnitude of the lakes water levels variations differ significantly. For lakes Balkhash, Issyk-Kul, Ladoga, Superior, and Erie, there is a general tendency for a decrease in water levels. For the remaining three lakes (Baikal, Onega, and Ontario), the opposite tendency has been noted: the levels of these lakes increased. Quantitatively, the range of changes in water levels on the lakes in question over the period of 1980-2008 ranged from -4 cm to +26 cm.
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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 | 0.002 |
| 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".