State role and involvement in determining wetland mitigation performance standards in the United States
Bibliographic record
Abstract
Wetlands are important ecosystems that contribute to the sustainability of global ecosystems and provide ecosystem functions and services to human civilization. However, many anthropogenic land use practices have led to the degradation of wetlands, making them globally imperiled ecosystems. Within the United States, wetland mitigation is a federally regulated restoration strategy that offsets and compensates for impacts on aquatic resources through restoration. Performance standards assess post-restoration ecosystem development and help regulate management actions. The primary objective of this study is to investigate the organization and interactions of states and federal agencies in determining wetland mitigation performance standards. Using a mixed method approach, including semi-structured interviews and online database reviews, we identify decision-making drivers from the state agency perspective. We develop a ranking classification of state legislation that references performance standards and describes guidance documents by type of authorship. Our findings detail the results of our inquiry into each state’s procedures, including performance standards, revealing diverse management approaches across the nation as states play various implementation and regulatory roles and are driven by collaboration and negotiation among regulators, state and federal legislation, and guidance documents. In addition, we found performance standards most often assess biotic characteristics, with vegetative criteria being the most common. This study synthesizes performance-standard determination and criteria derived from interviews across a spectrum of federal and state participants and a series of guidance documents. We have built a database of these criteria by state and theme to improve our understanding of the dynamic interplay between wetland mitigation science, practice, and policy. Our findings are discussed in the context of the 2023 Sackett vs. United States Environmental Protection Agency ruling.
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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 | 0.001 | 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".