Learning from a large-scale calibration effort of multiple lake temperature models
Bibliographic record
Abstract
Process-based lake temperature models, formulated on hydrodynamic principles, are commonly used to simulate water temperature, enabling one to test different scenarios and draw conclusions about possible water quality developments or changes in important ecological processes such as greenhouse gas emissions. Even though there are several models available, a systematic comparison regarding their performance is currently missing. In this study, we calibrated four different one-dimensional (1D) lake temperature models for a global dataset of 73 lakes to compare their performance with respect to reproducing water temperature, and we estimated parameter sensitivity for the calibrated parameters. The parameter values, model performance, and parameter sensitivity differed between lake models and between clusters that were defined based on lake characteristics. No single model performed best, with each model performing better than the others in at least some of the lakes. From the findings, we advocate the application of model ensembles. Nonetheless, we also highlight the need to further improve weather forcing data, individual models, and multi-model ensemble techniques.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".