Preliminary results of the tolerance to inorganic contaminants and phytoextraction potential of twelve ornamental shrub species tested on an experimental contaminated site
Bibliographic record
Abstract
In urban areas of China, several plant species are commonly used for ornamental purposes. Many of these plants have demonstrated a good capacity to resist these varied urban stresses, and it would be relevant to verify whether they can be grown on polluted sites and demonstrate some phytoremediation potential. Twelve ornamental shrub species were chosen to be tested for tolerance to inorganic contaminants and capacity to absorb and concentrate heavy metals in their aerial parts. A large split-plot trial comprising 20 plots was set up, and soil was spiked with different metals (Cu, Pb and Zn). In general, all twelve shrub species performed well regardless of the treatment. Two Hibiscus species, H. mutabilis and H. syriacus “Hamabo” were particularly productive. In terms of capacity to uptake metals, two of the best performing species were Spiraea japonica, for copper, and Nandina domestica, for lead. Bioconcentration and transfer factors were low. This could be related to weak development of the root systems in these recently established plants. Species with high yield, such as the two Hibiscus species, presented more interesting values in terms of quantity of metal extracted, and could eventually be recommended for decontamination of soils polluted by inorganics.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".