Novel ways to assess forested wetland restoration in North Carolina using ecohydrological patterns from reference sites
Bibliographic record
Abstract
ABSTRACT Restoring jurisdictional wetland hydrology does not ensure that the hydrologic conditions of any specific natural forested wetland community are recreated. This is especially problematic for a state like North Carolina, which has roughly two dozen different forested wetland communities. Because forested wetland communities align themselves across edaphic and hydrologic gradients, we suggest exploiting these relationships to guide restoration design and set performance standards. However, methods to compare hydrology between multiple reference sites and restored wetlands are lacking. To address this, we studied three different forested wetlands across 13 reference sites to determine which measures of water table levels, based on correlation with plant community composition, would be useful to distinguish these three communities. We then used the best measures of water table levels to assess two restoration sites and compare them with the reference sites. Our results showed that monthly median water table levels encompassing the start of the growing season had the strongest correlation with plant community composition, whereas roughly five other measures also had strong correlations. On the basis of the best measures, both of the restoration sites had water table levels that generally fell within the natural range of the reference sites. Because there was variation in water table levels across each restoration site, the different measures where useful to identify which areas were least similar to the reference sites and might need further monitoring in the future. On the basis of our results, we recommend using community‐specific measures of hydrology to guide and assess forested wetland restoration. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".