Lead and nickel accumulation in Iris pumila: Consideration of its usefulness as a potential bioindicator in the natural protected area of Deliblato Sands, Serbia
Bibliographic record
Abstract
In this work, we investigated the suitability of the threatened species, Iris pumila L., as a possible bioindicator of traffic pollution in its natural habitats (mostly protected areas and natural reserves). We examined its potential to accumulate lead (Pb) and nickel (Ni) in polluted habitats, and the genetic variability for this capability, since it is an important facet of bioindicator suitability. We estimated the concentrations of Pb and Ni in the leaves of 17 I. pumila clones (genotypes) grown in one of their natural habitats, the unpolluted semi-arid habitat of the protected Deliblato Sands Special Natural Reserve, and in the leaves of 18 Iris pumila full-sib families grown in an experimental plot in a heavily polluted urban location in Belgrade, Serbia. Comparison of the contrasting habitats by one-way ANOVA analysis showed that both Pb and Ni concentrations were significantly higher (six-fold) in the I. pumila leaves collected from plants grown in the polluted urban habitat. Two-way ANOVA (randomized block design) analysis performed on the full-sib families grown in the urban location failed to detect significant genetic variation for metal accumulation in I. pumila leaves. A significant block effect on the concentration of Ni in leaves was detected, indicating responsiveness to microenvironmental variability. These results suggest that I. pumila can serve as a good indicator of traffic pollution in protected areas. The response is stable since genetic variability of I. pumila populations does not appear to influence its role as an indicator greatly.
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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".