Early rhizosphere microbiome composition is related to the growth and <scp> <scp>Zn</scp> </scp> uptake of willows introduced to a former landfill
Bibliographic record
Abstract
Although plants introduced for site restoration are pre-selected for specific traits (e.g. trace element bioaccumulation, rapid growth in poor soils), the in situ success of these plants likely depends on the recruitment of appropriate rhizosphere microorganisms from their new environment. We introduced three willow (Salix spp.) cultivars to a contaminated landfill, and performed soil chemical analyses, plant measurements, and Ion Torrent sequencing of rhizospheric fungal and bacterial communities at 4 and 16 months post-planting. The abundance of certain dominant fungi was linked to willow accumulation of Zn, the most abundant trace element at the site. Interestingly, total Zn accumulation was better explained by fungal community structure 4 months post-planting than 16 months post-planting, suggesting that initial microbial recruitment may be critical. In addition, when the putative ectomycorrhizal fungi Sphaerosporella brunnea and Inocybe sp. dominated the rhizosphere 4 months post-planting, Zn accumulation efficiency was negatively correlated with fungal diversity. Although field studies such as this rely on correlation, these results suggest that the soil microbiome may have the greatest impact on plant function during the early stages of growth, and that plant-fungus specificity may be essential.
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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) | 0.000 | 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".