Using metagenomics to show the efficacy of forest restoration in the New Jersey Pine Barrens
Bibliographic record
Abstract
The Franklin Parker Preserve within the New Jersey Pine Barrens contains 5000 acres of wetlands habitat, including old-growth Atlantic white cedar (or AWC; Chamaecyparis thyoides) swamps, cranberry bogs, and former cranberry bogs undergoing restoration into AWC forests. This study showed that the C-use efficiency was greater in the old-growth AWC soils than in soils from 8-year-old mid-stage restored AWC stands, which were greater than found in soil from 4-year-old AWC stands-the latter two stands being restored from long-term cranberry bogs. A metagenomic analysis of eDNA extracted from these soils showed that the C-cycle trends were associated with increases in the relative numbers of DNA sequences from several copiotrophic bacterial groups (Bacteroidetes and Proteobacteria), complex C-decomposing fungal groups (Sordiomycetes, Mortierellales, and Thelephorales), and collembolan and formicid invertebrates. All groups are indicators of successionally more advanced soils, and critical for soil C-cycle activities. These data suggest that the restoration activities studied are enhancing critical guilds of soil biota, and increasing C-use efficiency in the soils of restored habitats, and that the use of metagenomic analysis of soil eDNA can be used in the development of assessment models for soil recovery of wetlands following restoration.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".