Summary performance of the Estuary and Lake Computer Model (ELCOM): application in the Laurentian and other Great Lakes
Bibliographic record
Abstract
The use of sophisticated three-dimensional (3D) hydrodynamic models is often required to simulate the spatial and temporal variability of water quality in large lakes. Recently, coupled lake–atmosphere models have also been developed to resolve the spatial distribution of the thermal behavior in lakes and to assess the feedback mechanisms at the air–water interface. In the studies summarized in this paper, the 3D Estuary and Lake Computer Model (ELCOM) acts as the hydrodynamic driver that provides temperature, salinity, and the transport fields that, if coupled with the Computational Aquatic Ecosystem Dynamics Model (CAEDYM), simulates nutrients, phytoplankton, zooplankton, and benthic habitat. This study presents a summary of the performance of ELCOM, and in an indirect form, serves as well as a corroboration of the strength or weakness of the coupled modeling and its ability to reproduce the thermal structure and circulation patterns, with examples from the Laurentian Great Lakes (Erie and Ontario), Northern Great Lakes (Great Slave Lake and Great Bear Lake), and Lake Winnipeg in Central Canada.
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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 | 0.001 |
| 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".