Historical datasets (1950–2022) of monthly water balance components for the Laurentian Great Lakes
Bibliographic record
Abstract
This study develops a 73-year dataset of water balance components from 1950 to 2022 for the Laurentian Great Lakes Basins. This is carried out using the Large Lakes Statistical Water Balance Model (L2SWBM), which provides a Bayesian statistical framework that assimilates binational input datasets sourced from the United States and Canada. The L2SWBM infers feasible water balance component estimates through this Bayesian framework by constraining the output with a standard water balance equation. The result is value-added time series, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. Therefore, the L2SWBM facilitates the understanding of discrepancies in datasets and hydroclimate parameters. This enhanced reliability stemming from coordinated data, with an understanding and quantification of uncertainty, could significantly boost confidence in decision support tools for water resources practitioners and policymakers. This joint effort advances scientific understanding and strengthens strategies and policies designed to bolster resilience in Great Lakes communities and its ecosystem in the face of a shifting climate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".