Winter thermal structure across the Laurentian Great Lakes
Bibliographic record
Abstract
The formation of winter stratification and thermal structure in general across the Great Lakes varies in character not just between lakes, but interannually within individual lakes. Three large datasets comprise all of the publicly available Great Lakes water temperature data that span both the winter and the entire water column. Multiple sites and multiple years of data are available for Lake Superior , as well as multiple years in Lake Huron and Lake Michigan, 2 years in Lake Ontario at multiple sites, and a single year at two sites in Lake Erie . The lakes show diverse manifestations of winter stratification, with Lake Superior reliably forming winter stratification, Lake Michigan rarely forming stratification, and Huron forming stratification in about half of the winters for which data are available (there is not enough data to evaluate this for Erie and Ontario). Whether a lake forms stratification or not in a given year is governed by how much heat a lake loses below the temperature of maximum density; a heat content of roughly −1 GJm −2 relative to the temperature of maximum density appears to be a threshold for the formation of winter stratification. Minimum heat content in a given year is a strong function of average winter air temperature. When combined with a historical database of basin-wide air temperature, the winter stratification threshold can be used to hindcast stratification formation in Superior, Huron, and Michigan over the last century, showing that Michigan and Huron are currently undergoing a climate-driven shift in stratification status.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".