Towards modeling data-poor lakes at the regional scale using parameters from data-rich lakes and relationships to lake characteristics
Bibliographic record
Abstract
Lakes that are pivotal for recreation and economically relevant activities are often remote and not very well studied, which hinders the application of predictive lake models for their management. Here, we provide an approach to simulate, by means of the process-oriented model MyLake, water temperature, ice cover duration, dissolved oxygen, and light attenuation in 198 data-poor lakes based on parameters obtained for a subgroup of 12 data-rich lakes and morphometric data. Specifically, the model is first calibrated using a genetic algorithm on well-studied lakes. Then, simple relationships between the fitted parameters and lake-catchment morphometric properties are derived. The results of simulations using fitted and derived parameters are then compared. The loss in goodness-of-fit, expressed as root mean square error (RMSE), incurred by using estimated rather than calibrated parameters, is 0.17 oC for water temperature and 0.82 mg L-1 for dissolved oxygen. These general relationships are then used to provide the model parameters for 198 data-poor lakes distributed throughout Sweden and model these lakes. Overall, this proof of concept allows simulating lakes selected based on their relevance for lake management rather than based on the availability of extensive field datasets.
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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 | 0.000 |
| 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".