Increasing maximum lake surface temperature under climate change
Bibliographic record
Abstract
Abstract Annual maximum lake surface temperature influences ecosystem structure and function and, in particular, the rates of metabolic activities, species survival and biogeography. Here, we evaluated 50 years of observational data, from 1966 to 2015, for ten European lakes to quantify changes in the annual maximum surface temperature and the duration above a potentially critical temperature of 20 °C. Our results show that annual maximum lake surface temperature has increased at an average rate of +0.58 °C decade −1 (95% confidence interval 0.18), which is similar to the observed increase in annual maximum air temperature of +0.42 °C decade −1 (95% confidence interval 0.28) over the same period. Increments in lake maximum temperature among the ten lakes range from +0.1 in the west to +1.9 °C decade −1 in the east. Absolute maximum lake surface water temperatures were reached in Wörthersee, 27.5 °C, and Neusiedler See, 31.7 °C. Periods exceeding a critical temperature of 20 °C each year became two to six times longer than the respective average (6 to 93). The depth at which water temperature exceeded 20 °C increased from less than 1 to more than 6 m in Mondsee, Austria, over the 50 years studied. As a consequence, the habitable environment became increasingly restricted for many organisms that are adapted to historic conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".