Monitoring of Air Pollution in Košice (Eastern Slovakia) Using Lichens
Bibliographic record
Abstract
The influence of air pollution on epiphytic lichens in Košice city has been studied. We observed differ-ences in number of species as well as lichen abundance at sites close to a steel factory south of the city, the city center, and peripheral parts north of the city. For the city center and sites close to steel factory, lichens more tolerant to pollution from Zone 3-4 were typical. However, on the north periphery of the city (site “Alpinka”) we found even Ramalina fastigiata, a typical member of zone 7, which include lichens very sensitive to air pollution [30]. We demonstrated by chlorophyll analysis of transplanted Hypogymnia physodes, that chlorophyll a degradation (expressed as ratio of OD 435/OD 415) negatively correlates with degree of lichen diversity and abundance at the studied sites. Using EDX-microanalysis we determined amounts of elements in lichen thalli of Lecanora chlarotera, Physcia tenella and bark of the tree Populus tremula (lichen substrate) near U.S. Steel in Košice due to determine the chemical nature of air pollution. Similarly, we analyzed the amount of these pollutants in con-trol lichens Flavoparmelia caperata, Ramalina fastigiata and Physcia aipolia, grown in northern peripheral parts of the city. We demonstrated possibilities to parallel the use of several methodologies in assessment of air pollution by lichens in urban areas with intensive industry.
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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 | 0.001 |
| 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".