Estimate of Cliff Recession Rates for a US Highway Located on a Sandstone Cliff over Lake Superior
Bibliographic record
Abstract
Coastal cliff erosion is a problem in many coastal regions including the Great Lakes of Canada and the United States. While data exists on the recession rates for oceanic cliffs, there is limited data for the fresh water cliff erosion. Currently, cliff recession is threatening a US highway (US-41) located on a 30 m sandstone cliff on the south shore of Lake Superior. The recession has advanced to a point where it is undercutting the guardrail system for the highway. A research program was conducted to determine the regression rate and when the highway should be relocated or if alternative methods of slope remediation can be performed allowing the scenic highway to remain in its current position. The cliff regression analysis includes investigating variations in shore platform widths, freeze thaw cycling, and other environmental factors, in addition to rock characteristics. Laboratory tests include point load testing, uniaxial compressive testing, rock quality designation (RQD), rock mass rating (RMR), and freeze-thaw durability. It was found that the following factors control the rate of the cliff regression, which was found to be about 0.15 feet/year: (1) deposition of mine waste at the base of the cliffs during the early 20th century and the subsequent removal by long shore currents; (2) rock weathering and water migration above low permeability layers accessing the cliff face; and (3) the development of the talus slope at the base of the cliff, which acts as a barrier to further regression.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".