Enlightening the Pathway of Phytoremediation: Ecophysiology and X-ray Fluorescence Visualization of Two Chilean Hardwoods Exposed to Excess Copper
Bibliographic record
Abstract
In the present climate emergency due to global warming, we are urged to move away from fossil fuels and pursue a speedy conversion to renewable energy systems. Consequently, copper (Cu) will remain in high demand because it is a highly efficient conductor used in clean energy systems to generate power from solar, hydro, thermal and wind energy across the world. Chile is the global leader in copper production, but this position has resulted in Chile having several hundred tailing deposits. We grew two Chilean native hardwood species, quillay (Quillaja saponaria Molina) and espino (Vachellia caven (Molina) Seigler & Ebinger, under three increasing Cu levels (0, 50, and 100 µM) for 6 months in a greenhouse setting. We measured growth, photosynthetic performance and elemental contents of leaves and roots to further evaluate their potential for phytoremediation. Growth of quillay was unaffected by Cu treatment but growth of espino was enhanced, as was its photosynthetic performance, indicating that espino may have an unusually high requirement for copper. Excess Cu was mostly restricted to the roots of both species, where X-ray fluorescence (XRF) mapping indicated some tendency for Cu to accumulate in tissues outside the periderm. Calcium oxalate crystals were prominently visible in XRF images of both species. Nickel (but not Cu) showed a concurrent distribution pattern with these crystals.
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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 | 0.001 |
| 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".