Silicon Isotope Analyses of Soil and Plant Reference Materials: An Inter‐Comparison of Seven Laboratories
Bibliographic record
Abstract
The use of silicon (Si) isotopes has led to major advances in our understanding of Si cycling in modern and past environments. This inter‐laboratory comparison exercise provides the community with the first set of soil and plant reference materials with an analytically challenging matrix containing organic material that is known to induce isotopic bias, for use as secondary reference materials in Si isotope measurement. Seven laboratories analysed four soil reference materials (GBW‐07401, GBW‐07404, GBW‐07407, TILL‐1) and one plant reference material (ERM‐CD281). Participating laboratories employed a range of chemical preparation methods and analytical setups but all analyses were performed by MC‐ICP‐MS. Irrespective of the chemical preparation method or analytical conditions, the results show excellent agreement among laboratories within 2s for at least three replicates. Data were combined together to calculate δ29Si and δ30Si mean values (relative to NBS 28) and their expanded uncertainties (U, coverage factor k = 2). The δ30Si values are as follow: GBW‐07401: −0.27 ± 0.06‰, GBW‐07404: −0.76 ± 0.12‰, GBW‐07407: −1.82 ± 0.17‰, TILL‐1: −0.16 ± 0.06‰ and ERM‐CD281: −0.28 ± 0.11‰. Also, a compilation of published data provides an up‐to‐date mean δ30Si for BHVO‐2 of −0.28 ± 0.08‰.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".