Editorial: “Metal Isotope Analytical Chemistry for Geological and Environmental Sample”
Bibliographic record
Abstract
Inorganic mass spectrometry plays a central role in the determination of isotopic compositions of metals in geological materials, as it is evident from the increasing number of publications in geoscience and environmental science. The isotopic analysis of both traditional radiogenic isotopes (Sr, Nd, Pb, Os etc.,) and stable isotopes (Li, Mg, Ca, Cr, Fe, Cu, Zn etc.,) is crucial in isotope geochemistry and geochronology. High precision analytical techniques based on TIMS, MC-ICP-MS, SIMS and LA-ICP-MS have been greatly driving the rapid developments in geoscience and environmental science over the past half century. Six papers are published in this special issue, and we regret that many other excellent manuscripts were not included due to the limitation of the number of pages available for this special issue. We are pleased to see novel and precise analytical methods developed for the isotopic analyses of Cu, Li and Sr, and the release of several new CRMs for Cu and Sr isotopic compositions for in situ analysis in this special issue. We thank all authors who have contributed to the one review article and five original research articles, presented in this special issue. We appreciate all reviewers’ efforts to maintain the high quality of reviewing process for these papers. We hope that you would enjoy reading this collection of articles.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".