Qualitative distribution of chemical elements in leaves of <i>Tillandsia</i> grown in urban and natural environments
Bibliographic record
Abstract
Epiphytic plants, such as those of the genus Tillandsia, accumulate elements from the air, reflecting the atmospheric composition. This study analyzed the species Tillandsia pohliana Mez, Tillandsia recurvata (L.) L., and Tillandsia loliacea Mart. ex Schult. & Schult.f. in urban environments and in surrounding preserved areas (natural environment), identifying, quantifying, and localizing the chemical elements in tissues via methods such as spectrometry (inductively coupled plasma mass spectrometry (ICP-MS)), electron microscopy (scanning electron microscopy (SEM)-energy dispersive spectrometer (EDS)), and light microscopy. The impact of the urban environment on plant metabolism was assessed using chlorophyll fluorescence analysis. The results revealed differences in the composition and location of the chemical elements in the leaves of the three species in the two environments. Plants grown in urban areas had relatively high concentrations of macronutrients, especially in peltate scales, but did not reach toxic levels. These concentrations of macronutrients did not appear to have any deleterious effects on photochemical rates and may even increase their photochemical efficiency, as observed for T. recurvata and T. pohliana. No higher concentrations of heavy or nonessential metals were observed, suggesting that the peltate scales function as efficient physical barriers to these elements. These results emphasize the complexity of plant responses to environmental conditions and the importance of continuous research to understand the impacts of urbanization and atmospheric pollution.
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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".