Simplifying and improving the extraction of nitrate from freshwater for stable isotope analyses
Bibliographic record
Abstract
Determining the isotopic composition of nitrate (NO(3)(-)) in water can prove useful to identify NO(3)(-) sources and to understand its dynamics in aquatic systems. Among the procedures available, the 'ion-exchange resin method' involves extracting NO(3)(-) from freshwater and converting it into solid silver nitrate (AgNO(3)), which is then analysed for (15)N/(14)N and (18)O/(16)O ratios. This study describes a simplified methodology where water was not pre-treated to remove dissolved organic carbon (DOC) or barium cations (added to precipitate O-bearing contaminants), which suited samples with high NO(3)(-) (≥ 00 μM or 25 mg L(-1) NO(3)(-)) and low DOC (typically < 17 μM of C or 5 mg L(-1) C) levels. % N analysis revealed that a few AgNO(3) samples were of low purity (compared with expected % N of 8.2), highlighting the necessity to introduce quality control/quality assurance procedures for silver nitrate prepared from field water samples. Recommendations are then made to monitor % N together with % O (expected at 28.6, i.e. 3.5 fold % N) in AgNO(3) in order to better assess the type and gravity of the contamination as well as to identify potentially unreliable data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".