Using turf transplants to reintroduce native forest understory plants into smelter‐disturbed forests
Bibliographic record
Abstract
This study investigated factors affecting transplantation success of native forest understory vegetation on metal‐contaminated soils. One year after transplantation along a gradient of historical Cu–Ni smelter pollution in Sudbury, Canada, community‐level characteristics of the transplanted plots and short‐term indicators expressing the performance of selected species were assessed. Thirty 16‐m2 plots were studied, each established with 45 transplanted 0.56 × 0.64−m turfs 10 cm thick, extracted from mixed‐hardwood forests slated for development. Species richness, diversity, and evenness were not affected by environmental conditions but short‐term responses of root growth and sexual reproduction of selected species to environmental variables indicate a need for long‐term monitoring. Root growth of Clintonia borealis (Aiton) Raf. and Gaultheria procumbens L. was positively related to soil temperature. Root growth of G. procumbens correlated negatively with soil Ni availability, but in all plots, root growth was comparable with the literature values from unaffected forests. Flowering frequency of G. procumbens correlated negatively with soil pH and positively with tree cover, corresponding to ecological requirements of this species. Soil had a negative effect on sexual reproduction of C. borealis. Unexpectedly, fruiting of C. borealis responded positively to Ni, and fruiting of Maianthemum canadense Desf. positively to As, possibly due to interdependencies among soil variables. The results are encouraging with respect to transplant success, as effects of smelter‐related variables were relatively minor, but species‐specific responses of the selected species to environmental factors indicate that species performance is dependent on site‐specific conditions, potentially influencing long‐term success of the transplants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".