BLUFF EROSION HAZARDS AND CONSTRUCTION SETBACKS ON THE GREAT LAKES COASTS OF THE UNITED STATES: A REVIEW
Bibliographic record
Abstract
Approximately 34 million people live within the North American Great Lakes Basin: ~32% of the Canadian population and ~8% of the US population.About 12 million of those people live on the Lake Erie coast of New York, Pennsylvania, Ohio, and Ontario.Unconsolidated Quaternary-age bluffs ranging in height from 1.5-55m dominate along 73km of the 123km Pennsylvania coast, and long-term records show that slow-continuous erosion is pervasive, and that rapid (but locally catastrophic) bluff failure is relatively infrequent.As a result, ~90% of the Lake Erie bluff coast in Pennsylvania is designated by the state as a Bluff Recession Hazard Area wherein regulations limit risky bluff-top development.A high degree of variability (space, time) in bluff-retreat rates exists because stratigraphy and geotechnical properties show variation due to materials, depositional geometries, post-depositional processes, hydrology, and anthropogenic influences.This makes it difficult to forecast the magnitude, frequency, and location of larger bluff-failure events and consequently makes pre-emptive mitigation efforts more challenging.Two methods are commonly used to establish coastal construction setbacks on Great Lakes bluff coasts: (i) the "AARRxT" method which uses a simple linear extrapolation of past bluff-change rates to estimate the future bluff position and the setback line for a building being constructed today; and (ii) the "AARRxT+" method which uses a similar approach but incorporates a slope stability factor and/or a relocation buffer.The limitation is that these deterministic methods assume that rates and magnitudes of processes driving change in the past will not change in the future, and they create the impression that bluff change is linear and more predictable than it is in reality.At the property and municipality scales, this makes hazard planning for continuous and catastrophic bluff failure particularly challenging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".