Using Native Woody Plants for Phytomanagement of Urban Technosols Contaminated by Wood Pole Preservatives
Bibliographic record
Abstract
Abstract Technosols are a major component of urban areas with little to no value, but many benefits can be gained by the use of native plants for their ecological reclamation. The aim of this study is to examine the capacity of North American native woody species to establish in and remediate a wood preservative‐contaminated technosol. Nine species are planted in mesocosms containing highly contaminated or non‐contaminated synthetic technosols. Remediation potential and plant physiology parameters (survival, photosynthesis and root growth) are monitored during one growing season. Results show an inverse relation between species performance ranking in the non‐contaminated technosol compared to ranking in contaminated soil. Overall, Salix bebbiana and Sambucus canadensis show good potential for phytomanagement of heterogenous contaminated technosols, offering the best compromise between performance in non‐contaminated and contaminated soils. Most species are able to phytoextract copper (Cu), with Rhus typhina, Alnus crispa, and S. bebbiana showing the highest Cu concentrations in stems. The use of multiple native species for technosol reclamation can be beneficial due to the heterogeneous nature of soil contaminants.
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 | 0.000 | 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) | 0.001 | 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".