Applications of zinc stable isotope analysis in environmental and biological systems: a review
Bibliographic record
Abstract
Zinc (Zn) is an essential trace element that is involved in both biotic and abiotic processes in Earth's surface environments. Over the last 20 years, advances in mass spectrometry instrumentation have enabled unprecedented high-precision Zn isotope abundance ratio determinations in natural and anthropogenic samples. Supported by a solid theoretical background inherited from the traditional non-metal stable isotopes (C, N, H, O, S), the understanding of the controlling factors of Zn isotope fractionation has rapidly evolved. In this article, we review the main applications and findings of Zn isotopes in a variety of scientific domains, including metal contamination, magmatic differentiation, plant uptake, weathering, global climate change, dietary and trophic chains, and biomedicine. The analytical aspects of Zn isotope determination in solid and water samples and the principles and mechanisms of Zn isotope fractionation are also reviewed. This work aims to provide a general yet in-depth panorama of Zn isotope chemistry and to demonstrate the versatility and potential of this isotope system for studying the biological, geological and chemical cycling of Zn.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".