Distribution of Zn in functionally different leaf epidermal cells of the hyperaccumulator <i>Thlaspi caerulescens</i>
Bibliographic record
Abstract
ABSTRACT The aim of this study was to show the potential of Thlaspi caerulescens in the cleaning‐up of a moderately Zn ‐contaminated soil and to elucidate tolerance mechanisms at the cellular and subcellular level for the detoxification of the accumulated metal within the leaf. Measured Zn concentrations in shoots were high and reached a maximum value of 83 mmol kg−1 dry mass, whereas total concentrations of Zn in the roots were lower (up to 13 mmol kg−1). In order to visualize and quantify Zn at the subcellular level in roots and leaves, ultrathin cryosections were analysed using energy‐dispersive X‐ray micro‐analysis. Elemental maps of ultrathin cryosections showed that T. caerulescens mainly accumulated Zn in the vacuoles of epidermal leaf cells and Zn was almost absent from the vacuoles of the cells from the stomatal complex, thereby protecting the guard and subsidiary cells from high Zn concentrations. Observed patterns of Zn distribution between the functionally different epidermal cells were the same in both the upper and lower epidermis, and were independent of the total Zn content of the plant. Zinc stored in vacuoles was evenly distributed and no Zn‐containing crystals or deposits were observed. From the elemental maps there was no indication that P, S or Cl was associated with the high Zn concentrations in the vacuoles. In addition, Zn also accumulated in high concentrations in both the cell walls of epidermal cells and in the mesophyll cells, indicating that apoplastic compartmentation is another important mechanism involved in zinc tolerance in the leaves of T. caerulescens.
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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".