Small lakes show muted climate change signal in deepwater temperatures
Bibliographic record
Abstract
Abstract Water temperature observations were collected from 142 lakes across Wisconsin, USA, to examine variation in temperature of lakes exposed to similar regional climate. Whole lake water temperatures increased across the state from 1990 to 2012, with an average trend of 0.042°C yr −1 ± 0.01°C yr −1 . In large (>0.5 km 2 ) lakes, the positive temperature trend was similar across all depths. In small lakes (<0.5 km 2 ), the warming trend was restricted to shallow waters, with no significant temperature trend observed in water >0.5 times the maximum lake depth. The differing response of small versus large lakes is potentially a result of wind‐sheltering reducing turbulent mixing magnitude in small lakes. These results demonstrate that small lakes respond differently to climate change than large lakes, suggesting that current predictions of impacts to lakes from climate change may require modification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".