Shemya Island: Surviving the Storm—Rebuilding for the Future
Bibliographic record
Abstract
In the fall of 2022, a team of marine engineering and construction experts embarked on a journey to confront a daunting task in the far reaches of Alaska’s Aleutian Island Chain in the Western Bering Sea. A sequence of severe storms had recently dealt with a devastating blow to a waterfront facility that is critical to the US military and the public. The severe storms overtopped the 77-year-old Fuel Pier facility with 30- to 40-ft waves causing extensive structural damage and leaving it vulnerable. The overall mission objective was simple: keep the structure standing; however, the path to achieving this goal was obstructed by a dense fog of unknowns, obstacles, and constraints, including enormous environmental loading demands, long-distance open ocean logistics, Munitions of Explosive Concern (MEC), limited weather windows, and a persistently relentless work environment. The engineering design process led to an effective engineering solution that was not initially anticipated. This paper presents the extreme design-build efforts that ensued under a USACE contract for the US Air Force to develop long-term repairs for the Eareckson Air Station Fuel Pier in Shemya, Alaska.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".