A novel method for estimating the onset of thermal stratification in lakes from surface water measurements
Bibliographic record
Abstract
Abstract High‐frequency surface water temperature measurements were analyzed for 17 annual data series from seven lakes to assess whether the onset of thermal stratification can be determined from time series of surface water temperature measurements alone. Current methods for estimating the start of thermal stratification require depth‐resolved temperature measurements, whereas many existing high‐frequency measurements are often limited only to the lake surface. In this study, we show that the magnitude of the diel surface water temperature range can be used to estimate the onset of thermal stratification. We assess the accuracy of using the diel range as an estimate of the onset of thermal stratification by applying two methods based on the calculation of (1) the absolute difference in the diel surface temperature range and (2) the magnitude of the diel range from wavelet analysis. Our study shows that the onset of thermal stratification can be accurately estimated by wavelet analysis with a root mean square error of 2.1 days and by the observed diel temperature range method with a root mean square error of 11.8 days. This approach enables existing, and future, high‐resolution surface water data sets to be used to estimate the onset of lake stratification. Furthermore, the continuously increasing observational powers of satellites may eventually result in surface water temperature being measured at a sufficiently high temporal resolution at the spatial scales of small lakes to allow the onset of thermal stratification to be estimated remotely.
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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 | 0.004 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".