A Murky Ruling Threatens the Fate of Millions of US Wetlands
Bibliographic record
Abstract
Abstract For decades, federal protections were extended to wetlands adjacent to “waters of the US” by the Clean Water Act. In its Sackett v. EPA ruling, however, the US Supreme Court redefined the meaning of “adjacent,” eliminating protections to wetlands without a continuous surface connection to these waters (i.e., geographically isolated wetlands, GIWs). Yet it remains unclear how this continuous surface test will work in reality, where ecological connectivity often extends beyond physical connectivity. Here, we calculate the number of US wetlands that could be considered geographically isolated depending upon the distance threshold used to define isolation (ranging from 1 m to 100 m from the nearest hydrological feature). Overall, we estimate that 27–45% of wetlands, at minimum, could be considered geographically isolated using this range of distance thresholds. Over 3 million wetlands are within 1–100 m of the nearest hydrological feature, making them most vulnerable to losing prior protections from the Clean Water Act. The Midwest and Northeast have the largest share of potential GIWs within this range. Freshwater emergent wetlands and forested/shrub wetlands make up the majority of these vulnerable wetlands, though this varies by state. Roughly 47% of these wetlands are located in states without state-level protections for GIWs. Our analysis highlights the heterogeneity of risk to wetlands across the country and the scale of the uncertainty imposed by the updated Sackett definition. State-level protections that are robust to changes in federal protections are urgently needed to secure the country’s wetlands from further pollution and destruction.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".