Can tree-ring chemistry be used to monitor atmospheric nanoparticle contamination over time?
Bibliographic record
Abstract
Industrial activities and human population growth have resulted in an unprecedented increase in the release of particulate matter (PM) into the environment. Nanoparticle (NP) contamination is widespread and affects all terrestrial and aquatic ecosystems, putting humans and environment at risk. Several studies on the impact of PM and NPs on human health have been conducted over the past two decades, but their effects on plants are still poorly understood. What happens to them in forest ecosystems and trees has yet to be explored. In this paper, we review the literature on the capacity of trees to be used as bioindicators and proxy recorders of past air pollution events. Current research indicates that ultrafine particles can be taken up and translocated to different parts of a tree by physical and chemical processes, as we present studies of plant uptake and translocation processes of NPs in trees. Tree-ring chemistry, i.e., dendrochemistry, has been successfully used to reconstruct trace metal deposition from a variety of sources of pollution, including cars, metal refineries and coal burning. The use of dendrochemistry in environmental monitoring seems promising particularly given the presence of recent development of analytical tools, and is likely to provide useful data on atmospheric NP contamination that could not be obtained from any other source. However, still relatively little is known about the dynamic relationships between NPs and trees. We therefore discuss what future dendrochemical research is needed to make dendrochemical analyses as accurate as possible for monitoring atmospheric nanoparticle contamination over time.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".