EVALUATING TRENDS AND PATTERN OF GLACIAL ISOSTATIC ADJUSTMENT NEAR LAKE SUPERIOR
Bibliographic record
Abstract
The motion of the ground beneath and adjacent to Lake Superior continues to be influenced by the long-gone Laurentian Ice Sheet.The rate and pattern of vertical ground movement, glacial isostatic adjustment (GIA), is related to many factors that include variations in ice thickness and duration during the oscillatory retreat of the Laurentian Ice Sheet.Many previous research projects have recorded data of, or associated with, GIA by various methods; however, considering the different time periods examined by different research projects, the accuracy and consistency of these data is unknown.Hence, these data need to be analyzed and compiled to provide one view of GIA near Lake Superior.Here we present data collected from two sources, global positioning system (GPS), lake level gauges.Published rates of GIA from GPS stations surrounding Lake Superior were selected from a dataset covering North America.These data were then plotted and contoured to derive a rate and pattern of GIA based upon GPS data spanning recent decades.Water level gauge data for Lake Superior was updated from 2006 and reanalyzed following methods used in the most recent International Upper Great Lakes Study.This provided a view of GIA based upon water level gauge data that extended many decades before GPS data.After each source was analyzed independently, these two results were then compared between each other.For future work, these results can be compared with a rate and pattern of GIA provided by analyzing ancient shorelines or strandplains of beach ridges that are several millennia old adjacent to Lake Superior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".