Some considerations in the reconstruction of lead levels using laser ablation: lessons from the design stage of dendrochemistry study, St.John's, Canada
Bibliographic record
Abstract
Study of soils in St. John’s, Canada showed elevated Pb levels representing a potential ex-posure risk for young children. Old trees growing in the city present a potential annually-resolved record of Pb levels over past centuries that provides important temporal and spatial dimensions to Pb exposure risk assessment. This paper reports the results of our analytical tests to develop a fast, relia-ble and cost-efficient method using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) for measuring Pb concentration in annual tree rings from available tree species. Our tests focused on approaches to sample preparation as they affect the laser ablation process, the relative merits of the ablation sampling method, and the response of our available tree species, which have contrasting wood structures, to laser ablation. The range of annual Pb concentrations (ppm) measured for each of the study species were as follows: spruce (0.18–6.42); elm (0.12–7.91); and horse chestnut (0.40–14.09). Our results demonstrate that the cutting procedure for preparing tree cores produced the most consistent Pb concentrations of the three methods, although they each displayed problematic anomalies. The selection of the best laser ablation technique appears to be highly dependent on study species and goals. In general, spot analysis permits detailed and targeted studies of tree-ring struc-tures, but requires careful sampling attention for species with complex wood anatomy. The line scan method is ideal for reconstructing annually resolved element concentrations from trees and to some degree mitigates the complicating issue of intra-ring variability. Horse chestnut was determined to be the best of the available tree species because it exhibited a good response to laser ablation and pro-duced the lowest intra-ring variations in Pb concentration.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".