Global data set of long-term summertime vertical temperature profiles in 153 lakes
Bibliographic record
Abstract
Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".