Identifying ecohydrological patterns in natural forested wetlands useful to restoration design
Bibliographic record
Abstract
ABSTRACT Wetland restoration projects across the United States commonly lack the species and hydrology of the targeted community being restored. A better understanding of natural wetlands would help restoration project designers achieve community composition and hydrologic regimes that more closely resemble natural conditions. We investigated two different methods to identify patterns in hydrologic regime useful to restoration design. The first method was based on success criteria commonly used by North Carolina wetland mitigation projects. The second method was based on The Nature Conservancy's Indicators of Hydrologic Alteration. To test these two methods, we collected hydrologic and compositional data across the natural variation of nonriverine wet hardwood forest stands, a rare wetland community type commonly restored in North Carolina. Our results show that hydrologic parameters from the first method, based on current success criteria used in North Carolina, are not related to community composition. Hydrologic parameters from the second method, e.g. the maximum water table level over a 3‐day span, explained much of the variation in nonriverine wet hardwood forest community composition. Our results found that clear relationships do exist between hydrologic regime and community composition. Hydrologic parameters that are related to community composition need to be used as success criteria in future restoration designs. This will ensure that projects establish the appropriate hydrologic regime necessary to foster the desired wetland community type. Copyright © 2011 John Wiley & Sons, Ltd.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".