Cadmium variability in leaves of a <i>Salix fragilis</i>: simulation and implications for leaf sampling
Bibliographic record
Abstract
Plant analysis is a valuable tool to evaluate the pollution level. However, leaf sampling is complicated because of the high variability within the crown. To investigate the variability of cadmium (Cd) in the leaves of a tree, we sampled one Salix fragilis L. at 292 locations, each with a volume of 0.3 × 0.3 × 0.3 m. The Cd concentration was found to be normally distributed within a range from 2.4 to 10.6 mg·kg1 dry mass (DM), with an average of 6.3 mg·kg1 DM. A trend was found with high values in the lower parts of the crown and low Cd concentrations at the top. After removal of this trend the residuals showed a clear spatial structure modelled by a variogram. The Cd distribution in the leaves of the entire tree was predicted by sequential indicator simulation. These results were used to evaluate the current sampling strategy for tree leaves, i.e., sampling sun leaves of the upper third of the tree crown. The latter procedure was found to yield biased estimates of the average Cd concentration as well as the risk of exceeding a contamination threshold. An alternative sampling procedure is proposed. This procedure investigates whether a trend is present. Once the height where sampling will result in a correct statement of the tree's pollution is located, the rest of the stand could be sampled at this height.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".