Sinking Airports: A Glance at the State of US Transport Infrastructure
Bibliographic record
Abstract
Abstract Land subsidence poses a growing challenge to the operational safety and structural integrity of global air transport infrastructure. This study assesses the impact of differential land subsidence on airport runways using cutting‐edge Interferometric Synthetic Aperture Radar (InSAR) data across 15 major U.S. airports, providing an estimate of potential foundational damage caused by settlement due to natural and anthropogenic factors. Our findings show San Francisco International Airport experiences the fastest subsidence rate of 9.2 ± 0.2 mm/year, while Los Angeles International Airport has the slowest subsidence rate of 2.0 ± 0.2 mm/year. While 96.1% of runway areas fall under low damage risk, 3.9% are at medium to very‐high (VH) risk, with 3.5 million m 2 exposed to subsidence rates exceeding 5 mm/year and 13,950 m 2 classified as being at high to VH damage risk. Although no accidents have been directly linked to subsidence, increasing maintenance costs underscore the need for proactive monitoring. InSAR provides a near real‐time, cost‐effective solution for detecting infrastructure vulnerabilities, offering a non‐intrusive approach to enhancing airport resilience and operational safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".