Great Lakes monthly water balance components from the Large Lakes Statistical Water Balance Model (L2SWBM)
Bibliographic record
Abstract
**Note that an updated version of the data (v3.0) was uploaded on October 2, 2024, which supersedes earlier versions** These data sets are the results of leveraging bi-national data and the Large Lakes Statistical Water Balance Model (L2SWBM) specifically tailored for the Laurentian Great Lakes to produce value-added time series of water supply components, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. The model serves as a new cornerstone for bi-national coordination of hydrologic data throughout this international transboundary basin, providing an improved means of capturing data patterns, revealing seasonal variabilities, as well as short-term and long-term trends. This repository includes monthly output from the L2SWBM. Output datasets include over-lake precipitation, over-lake evaporation, lateral tributary inflow (runoff), connecting channel flow (cms and also included in mm normalized to lake area), diversion flow (cms and also included in mm normalized to lake area), and component net basin supply. Data is included for lakes Superior, Michigan-Huron, Erie, and Ontario. This version contains data from 1950 to 2022.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.025 |
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".