Geotechnical Effects of 2018 Hurricane Florence
Bibliographic record
Abstract
The Geotechnical Extreme Events Reconnaissance Association (GEER) Florence team of engineers deployed to southeastern North Carolina and northeastern South Carolina following Hurricane Florence with the GEER mission to “collect perishable postdisaster data that can be useful in advancing our understanding of extreme events,” and specifically, the effects of the extreme events on the geotechnical features of infrastructure. Florence’s impacts were significant and widespread because of the storm’s slow progression (4 days) across and massive rainfall (up to 34 in) on the study area. The team visited 23 dams, one levee system, seven bridges, eight roadway sites, two railroad impact areas, coastal sites in four counties, and one cemetery slope. This paper summarizes observations made by the team that characterize the event and the geotechnical infrastructure impacts along with lessons learned, both old and new. Event characteristics of note included rainfall, resulting flood flows, and impact location clusters. Infrastructure impact observations generally relate to the capacity of various structures to survive the flood flows, which typically overtopped most of the damaged structures. Lessons learned ranged from the obvious need for suitably sized spillways and culverts, bridge scour protection, and levee closures to smaller scale erosion characteristics of various infrastructure soils on dams with vegetation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".