Development and validation of in-situ and laboratory X-ray fluorescence (XRF) spectroscopy methods for moss biomonitoring of metal pollution
Bibliographic record
Abstract
Metals are among the pollutants of highest concern in urban areas due to their persistence, bioavailability and toxicity. High concentrations of metals threaten aquatic ecosystem functioning and biodiversity, as well as human health. High-resolution estimates of pollutant sources are required to mitigate exposure to toxic compounds by identifying the specific locations and associated site characteristics where the deposition of metals is greatest. Mosses have been widely used as low-cost biological monitors of metal pollution for decades, because they readily accumulate pollutants over time, reflecting long term pollution levels. However, spectroscopic techniques to determine the concentration of metal pollutants in moss samples still require expensive instrumentation and involve time consuming sample preparation protocols with heavy use of reagents. Here we present protocols to perform in-situ and laboratory X-ray fluorescence (XRF) spectroscopy of epiphytic moss as rapid, low-cost, and accurate alternatives to conventional metal pollution biomonitoring. We also report on a preliminary validation of the measurements using mass fractions determined by inductively coupled plasma atomic emission spectroscopy (ICP-OES) as reference. •XRF measurements are taken from moss directly on tree trunks in less than five minutes. •Grinding and pelletizing of moss enables definitive quantitation (R 2 >0.90) of metals through portable XRF.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".