Combining alders, frankiae, and mycorrhizae for the revegetation and remediation of contaminated ecosystems
Bibliographic record
Abstract
Alder shrubs and trees that are capable of forming symbioses with mycorrhizal fungi and the nitrogen-fixing actinomycete Frankia sp. are particularly hardy species found worldwide in harsh and nutrient-deficient ecosystems. The mycorrhizal symbiosis may assist alders in nutrient and water uptake, while the actinorhizal symbiosis provides assimilable nitrogen. It is through these highly efficient symbioses, in which microsymbionts benefit from plant photosynthates, that actinorhizal plants such as alders colonize poor substrates, enrich soil, and initiate plant succession. These natural capabilities, combined with careful screening of microsymbionts and host plants, may prove useful for the rehabilitation of disturbed ecosystems. Although alders have been used extensively at industrial scales in forestry, nurse planting, and contaminated land revegetation, relatively little research has focussed on their actinorhizal and mycorrhizal plant–microbe interactions in contaminated environments. To study such a topic is, however, critical to the successful development of phytotechnologies, and to understand the impact of anthropogenic stress on these organisms. In this review, we discuss two alder-based phytotechnologies that hold promise: the stimulation of organic contaminant biodegradation (rhizodegradation) by soil microflora in the presence of alders, and the phytostabilization of inorganic contaminants. We also summarize the plant–microbe interactions that characterize alders, and discuss important issues related to the study of actinorhizal and (or) mycorrhizal alders for the rehabilitation of disturbed soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".