Tracing Source Pollution in Soils Using Cadmium and Lead Isotopes
Bibliographic record
Abstract
Tracing the source of heavy metals in the environment is of key importance for our understanding of their pollution and natural cycles in the surface Earth reservoirs. Up to now, most exclusively Pb isotopes were used to effectively trace metal pollution sources in the environment. Here we report systematic variations of Cd isotope ratios measured in polluted topsoils surrounding a Pb-Zn refinery plant in northern France. Fractionated Cd was measured in soil samples surrounding the refinery, and this fractionation can be attributed to the refining processes. Despite the Cd isotopic ratios being precisely measured, the obtained uncertainties are still large compared to the total isotopic variation. Nevertheless, for the first time, Cd isotopically fractionated by industrial processes may be traced in the environment. On the same samples, Pb isotope systematics suggested that materials actually used by the refinery were not the major source of Pb in soils, probably because refined ore origins changed over the 100 years of operation. On the other hand, Cd isotopes and concentrations measured in topsoils allowed identification of three main origins (industrial dust and slag and agriculture), assuming that all Cd ores are not fractionated, as suggested by terrestrial rocks so far analyzed, and calculation of their relative contributions for each sampling point. Understanding that this refinery context was an ideal situation for such a study, our results lead to the possibility of tracing sources of anthropogenic Cd and better constrain mixing processes, fluxes, transport, and phasing out of industrial input in nature.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".