Rise of Great Lakes Surface Water, Sinking of the Upper Midwest of the United States, and Viscous Collapse of the Forebulge of the Former Laurentide Ice Sheet
Bibliographic record
Abstract
Abstract Great Lakes water levels rose 0.7–1.5 m from 2013 to 2019, increasing surface water volume by 285 km3. Solid Earth's elastic response to the increased mass load is nearly known: The Great Lakes floor fell 8–23 mm, and the adjacent land fell 3–14 mm. Correcting GPS measurements for this predicted elastic loading (1) straightens position‐time series, making the evolution of position more nearly a constant velocity and (2) reduces estimates of subsidence rate in Wisconsin, Michigan, and southern Ontario by 0.5–2 mm/yr, improving constraints on postglacial rebound. GPS records Wisconsin and Michigan to have subsided at 1–4 mm/yr. We find this sinking to be produced primarily by viscous collapse of the former Laurentide ice sheet forebulge and secondarily by elastic Great Lakes loading. We infer water on land in the Great Lakes watershed to be total water change observed by GRACE minus Great Lakes surface water smeared by a Gaussian distribution. Water stored on land each year reaches a maximum in March, 6 months before Great Lakes water levels peak in September. The seasonal oscillation of water on land in the Great Lakes basin, 100 km3 (0.20 m water thickness), is twice that in a hydrology model. In the seasons, groundwater in the Great Lakes watershed increases by 60 km3 (0.12 m) each autumn and winter and decreases by roughly an equivalent amount each spring and summer. In the long term, groundwater volume remained constant from 2004 to 2012 but increased by 50 km3 (0.10 m) from 2013 to 2019.
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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 | 0.000 | 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".