Modelling phytoremediation: Concepts, methods, challenges and perspectives
Bibliographic record
Abstract
Phytoremediation can be an effective approach for the removal, immobilization, mineralization, or detoxification of various contaminants in soils or water, including inorganic and organic pollutants, and radioisotopes. Although phytoremediation has been proved in the last decades, its performance is uncertain due to complex interactions among soil, water, plants, weather, microorganisms, and pollutants, leading to underutilizing globally. This paper aims to review representations and methods of quantifying key phytoremediation processes in modelling phytoremediation. We examine the structures, methods, and ability of phytoremediation models that characterize the biogeochemical, hydrological, and phenological processes accountable for phytoremediation dynamics, along with discussions about their advantages and limitations. Then, we identify the knowledge gaps and challenges of incorporating biogeochemical, hydrological, and phenological processes into phytoremediation models in contaminated sites and representing spatial heterogeneity and temporal variability in large-scale applications. The existing phytoremediation models find it difficult to predict the phytoremediation length under real environmental conditions if such a length is key for the assessment of phytoremediation performance and cost. Finally, we explore opportunities to integrate the current knowledge from other disciplines, such as soil, agriculture, ecology, and plant research in a competition-based model and point out key research priorities for the effective integration of knowledge on physical, chemical, and biological processes in modelling phytoremediation, including biogeochemical processes and agro-practices. More studies also need to consider immobilization, mineralization and detoxification.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".