Assessing Simple Versus Complex Restoration Strategies for Industrially Disturbed Forests
Bibliographic record
Abstract
Abstract Assessing the community‐level consequences of ecological restoration treatments is essential to guide future restoration efforts. We compared the vegetation composition and species richness of restored sites that received a range of restoration treatments and those of unrestored sites that experienced varying levels of disturbance. Our study was conducted in the industrially degraded landscape surrounding Sudbury, Ontario, Canada. The Great Lakes–St. Lawrence Forest once present in this area was degraded through logging, mining, and smelting activities beginning in the late 1800s until restoration of the most visibly degraded areas began in 1974. Restoration treatments ranged from simple abiotic enhancements to complex, multistage revegetation treatments using native and non‐native species, which included fertilizing, spreading of ground dolomitic limestone, understory seeding, and tree planting. Canonical correspondence analysis was used to determine which restoration treatments explained differences in the community structure among sites. We found that native understory vascular species richness was similar in restored sites that received more complex restoration treatments and unrestored sites that were mildly disturbed; however, the role of planted trees and non‐native species in the restored communities remains unclear. Understory vascular seeding played a key role in determining community composition of vascular understory and overstory communities, but the time since restoration commenced was a more important factor for nonvascular communities because they received no direct biotic enhancements. The use of non‐native species in the vascular seed mix seems to be slowly encouraging the colonization of native species, but non‐natives continue to dominate restored sites 25 years after restoration began.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".