Prediction of surface temperature in lakes with different morphology using air temperature
Bibliographic record
Abstract
Temperature of the surface layer of temperate lakes is reconstructed by means of a simplified model on the basis of air temperature alone. The comparison between calculated and observed data shows a remarkable agreement (Nash–Sutcliffe efficiency indices always larger than 0.87, mean absolute errors of approximately 1°C) for all 14 lakes investigated (Mara, Sparkling, Superior, Michigan, Huron, Erie, Ontario, Biel, Zurich, Constance, Garda, Neusiedl, Balaton, and Baikal, in west‐to‐east order), which present a wide range of morphological and hydrological characteristics. Differently from a pure heat flux balance approach, where the different fluxes are determined on the basis of independent relationships, the input data directly inform parameters of a simple model that, in turn, provides meaningful information about the properties of the real system. The dependence of the model parameters on the main morphological indicators is presented, which allows for a quantitative description of the strong influence of the mean depth of the lake on the thermal inertia and the hysteresis pattern between air and lake surface temperatures.
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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 | 0.000 | 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".