Willow Aboveground and Belowground Traits Can Predict Phytoremediation Services
Bibliographic record
Abstract
The increasing number of contaminated sites worldwide calls for sustainable remediation, such as phytoremediation, in which plants are used to decontaminate soils. We hypothesized that better anchoring phytoremediation in plant ecophysiology has the potential to drastically improve its predictability. In this study, we explored how the community composition, diversity and coppicing of willow plantations, influenced phytoremediation services in a four-year field trial. We also evaluated how community-level plant functional traits might be used as predictors of phytoremediation services, which would be a promising avenue for plant selection in phytoremediation. We found no consistent impact of neither willow diversity nor coppicing on phytoremediation services directly. These services were rather explained by willow traits related to resource economics and management strategy along the plant "fast-slow" continuum. We also found greater belowground investments to promote plant bioconcentration and soil decontamination. These traits-services correlations were consistent for several trace elements investigated, suggesting high generalizability among contaminants. Overall, our study provides evidence, even using a short taxonomic (and thus functional) plant gradient, that traits can be used as predictors for phytoremediation efficiency for a broad variety of contaminants. This suggests that a trait-based approach has great potential to develop predictive plant selection strategies in phytoremediation trials, through a better rooting of applied sciences in fundamental plant ecophysiology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".