Recommendations for offline combustion‐based nitrogen isotopic analysis of silicate minerals and rocks
Bibliographic record
Abstract
Rationale Due to isotope fractionations during partial nitrogen release from minerals and rocks, the complete extraction of nitrogen for analysis is crucial to ensure high‐quality nitrogen isotopic data. However, the appropriate nitrogen extraction conditions (e.g. temperature, duration) have not been established for most silicate minerals and rocks. Methods Nitrogen in a number of common minerals and rocks was extracted using the most robust sealed‐tube offline combustion techniques, purified and quantified in a custom‐made metal manifold, and carried by helium gas to an isotope ratio mass spectrometer for isotopic measurement at nanomolar nitrogen level. Each mineral or rock was combusted in a variety of temperature and duration conditions to compare the nitrogen yields and isotopic compositions. Results The nitrogen yields and isotopic compositions of minerals and rocks are strongly affected by combustion temperature and duration. The optimal combustion temperature is lowest for cyclosilicate minerals, followed by phyllosilicate, tectosilicate and inosilicate minerals. Preheating of samples can induce significant nitrogen loss and δ 15 N shift. Heating of samples above their optimal temperatures may cause nitrogen re‐assimilation by the residual mineral or rock. Conclusions Each mineral or rock has a characteristic optimal temperature and duration for complete nitrogen release. Preheating, under‐heating or over‐heating can cause nitrogen loss and isotopic shift. Therefore, we recommend using the offline combustion techniques and the optimal combustion conditions obtained in this study for nitrogen quantification and isotopic analysis of silicate minerals and rocks.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.027 |
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".