Land subsidence risk to infrastructure in US metropolises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Land subsidence is a slow-moving hazard with adverse environmental and socioeconomic consequences worldwide. While often considered solely a coastal hazard due to relative sea-level rise, subsidence also threatens inland urban areas, causing increased flood risks, structural damage and transportation disruptions. However, spatially dense subsidence rates that capture granular variations at high spatial density are often lacking, hindering assessment of associated infrastructure risks. Here we use space geodetic measurements from 2015 to 2021 to create high-resolution maps of subsidence rates for the 28 most populous US cities. We estimate that at least 20% of the urban area is sinking in all cities, mainly due to groundwater extraction, affecting ~34 million people. Additionally, more than 29,000 buildings are located in high and very high damage risk areas, indicating a greater likelihood of infrastructure damage. These datasets and information are crucial for developing ad hoc policies to adapt urban centers to these complex environmental challenges. Ohenhen et al. used space geodetic measurements to rigorously quantify land subsidence in the 28 most populous US cities. They find that over 20% of the area in each city is sinking, affecting approximately 34 million people and placing more than 29,000 buildings at high risk of damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it