Effect of Storage on the Isotopic Composition of Nitrate in Bulk Precipitation
Bibliographic record
Abstract
Stable isotopic analysis of atmospheric nitrate is increasingly employed to study nitrate sources and transformations in forested catchments. Large volumes have typically been required for delta18O and delta15N analysis of nitrate in precipitation due to relatively low nitrate concentrations. Having bulk collectors accumulate precipitation over an extended time period allows for collection of the required volume as well as reducing the total number of analyses needed to determine the isotopic composition of mean annual nitrate deposition. However, unfiltered precipitation left in collectors might be subject to microbial reactions that can alter the isotopic signature of nitrate in the sample. Precipitation obtained from the Turkey Lakes Watershed was incubated under conditions designed to mimic unfiltered storage in bulk precipitation collectors and monitored for changes in nitrate concentration, delta15N, and delta18O. Results of this experiment indicated that no detectable nitrate production or assimilation occurred in the samples during a two-week incubation period and that atmospheric nitrate isotopic ratios were preserved. The ability to collect unfiltered precipitation samples for an extended duration without alteration of nitrate isotope ratios is particularly useful at remote study sites where daily retrieval of samples may not be feasible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".