The coupled study of metal concentrations and electron paramagnetic resonance (EPR) of lichens (Hypogymnia physodes) from the Świętokrzyski National Park—environmental implications
Bibliographic record
Abstract
SO 2 , NO x , and metals (including Cd, Cu, Pb, Zn, Mn, Mg, Fe) present in airborne particulate matter are a major threat to preserving good air quality. The complicated pathways and transformation processes that can change their physical/chemical state in the atmosphere renders identifying their origin extremely difficult. With the objective of alleviating this difficulty, we identified and characterized potential local and regional sources of atmospheric pollutants using bioindicators ( Hypogymnia physodes ) from the Świętokrzyski National Park (SE Poland): 20 lichen samples were collected during winter (February; heating period) and summer (June; vegetative period) seasons and analyzed for metal contents and free radicals concentrations. Our results indicate that the highest gaseous pollutant levels were observed during the heating season, along roads (NO 2 ) and at the highest elevation (SO 2 ). The semiquinone/phenoxyl radical concentrations correlated during the heating season with the atmospheric SO 2 : ln (free radicals concentrations) = 0.025 SO 2atmosphere + 39.11. For Mn/Fe ≥ 2, the electron paramagnetic resonance (EPR) spectra presented a hyperfine splitting. Results showed that since 1994 metal concentrations increased for Cd, Mn, and Mg, Fe remained somewhat constant for Zn and Cu but slightly decreased for Pb, in agreement with the phasing out of lead in gasoline. Finally, a principal component analysis (PCA) identified two main factors controlling variability within the analyzed parameters: air pollutants transport over long distances and local fuel combustion by both transport and home heating.
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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) | 0.000 | 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".