Navigating Great Lakes Hydroclimate Data
Bibliographic record
Abstract
Despite the fact that the Great Lakes contain roughly 20% of the world's surface freshwater, there is a relatively limited body of recent work in peer reviewed literature that addresses recent trends in lake levels. This work is largely coming from a handful of authors who are most well-versed in the complexities of monitoring and modeling in a basin that spans an international border and contains vast areas of surface water connected by both natural and managed connecting channel flows. At the same time, the recent dramatic changes from record low water levels in the early 2010's to record high water levels across the Great Lakes in 2019 and 2020 have brought significant attention to the hydroclimatic conditions in the basin, underscoring the need to bring new approaches and diverse perspectives (including from outside the basin) to address hydroclimate research challenges in the Great Lakes. Significant effort has led to advancements in data and model coordination among U.S. and Canadian federal agencies throughout the decades, and at the same time research from the broader community has led to higher resolution gridded data products. In this paper, we aim to present the current state of data and models for use in hydrological simulation with the objective of providing a guide to navigating the waters of Great Lakes hydroclimate data. We focus on data for use in modeling water levels, but we expect the information to be more broadly applicable to other hydroclimate research. We approach this by including perspectives from both the Great Lakes water management community and the broader earth science community.
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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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".