Variation in metal concentration across tree organs and stands: implications for reference specimen development
Bibliographic record
Abstract
Air pollution is a major environmental and public health issue in urban areas. Biomonitoring using trees has been widely used to determine atmospheric pollution due to its advantages of low cost, high spatial distribution, and accumulation of a wide range of air contaminants. However, challenges exist in applying the data obtained from biomonitoring trees, including differences in pollutant concentrations in samples of the same species from different locations, and the influence of biological and environmental parameters on metal accumulation. This study aims to inform the design of biomonitoring sampling for urban air quality studies and increase their applications to exposure assessment. Specifically, the study evaluates the variation in metal concentration in tree organs of two regionally relevant tree species (Picea pungens and Populus Alba), explores the implications of different aspects of field sampling, such as variation within tree stands, tree age, and develops a reference specimen to establish threshold values for differentiating between background levels and increased exposure. The results provide insight into the complexity of using biomonitoring trees for exposure assessment and the importance of considering multiple factors in study design.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".