Elemental Composition Analysis of Plants and Composts Used for Soil Remediation by Laser‐Induced Breakdown Spectroscopy
Bibliographic record
Abstract
Laser‐induced breakdown spectroscopy (LIBS) is a fast and reliable technique suitable for the simultaneous qualitative and quantitative analysis of major and trace elements in samples of various nature and origin. In last decades, the use of metal accumulator plants, in combination with compost, has become a cheap and sustainable alternative technique to lower soil contamination by toxic heavy metals. In the present work, the LIBS technique has been applied to measure the concentrations of selected elements, including Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Pb, Sr, and Zn, in two composts of different origin and nature and four accumulator plant species (Atriplex halimus, Brassica alba, Brassica napus, and Eruca vesicaria). The plant samples were analyzed either as bulk plant material or as specific organs (i.e. shoots and roots). The concentrations measured by LIBS were assessed by complementary induced coupled plasma‐optical emission spectroscopy. The significant correlation found between the data obtained by the two techniques (R = 0.732–0.999) supports the feasibility of LIBS for fast screening of major, trace and toxic elements in plant and compost samples. In conclusion, the LIBS technique shows promising for further applications in soil remediation as well as in agriculture.
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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 | 0.001 | 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".