Effects of large-scale restoration on understory plant communities in an industrial landscape
Bibliographic record
Abstract
Starting in the mid 1970’s, researchers, industry leaders, and residents collaborated on one of the world’s largest regreening programs in the industrial region of Sudbury, Canada. The Sudbury Regreening Recipe included the application of crushed dolomitic limestone, nitrogen–phosphorus–potassium fertilizers, grass–legume seed mixtures across 8200 ha, and subsequent tree planting across 25 000 ha of acidic metal-contaminated land. The current study evaluated shifts in understory vegetation diversity and soil geochemistry on a chronosequence of sites treated the same way between 1982 and 2012. Fifty-six plant species were identified across the 24 sites, only four of which were planted in the initial remediation effort. Key factors influencing plant community composition and diversity were related to shifts in soil properties over time: bulk density, LFH layer depth, and mineral soil horizon pH. Plant communities differed with stand age and rocky sites had significantly different plant communities and lower canopy cover than less rocky sites. Mineral soil horizon pH increased with age, reflecting the movement of applied dolomite in soil. Despite high concentrations of total copper and nickel in soil, plant succession patterns were generally similar to those in naturally recovering forests demonstrating the overall success of the restoration program.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".