Measurement of <sup>13</sup>C and <sup>15</sup>N Isotopic Composition on Nanomolar Quantities of C and N
Bibliographic record
Abstract
We describe a trapping and chromatography system that cryogenically removes CO(2) and N(2) generated from sample combustion in an elemental analyzer (EA) and introduces these gases into a low-flow helium carrier stream for isotopic analysis. The sample size required for measurement by this system (termed nano-EA/IRMS) is almost 3 orders of magnitude less than conventional EA analyses and fills an important niche in the range of analytical isotopic methods. Only 25 nmol of N and 41 nmol of C are needed to achieve 1.0 per thousand precision (2sigma) from a single measurement while larger samples and replicate measurements provide better precision. Analyses of standards demonstrate that nano-EA measurements are both accurate and precise, even on nanomolar quantities of C and N. Conventional and nano-EA measurements on international and laboratory standards are indistinguishable within analytical precision. Likewise, nano-EA values for international standards do not differ statistically from their consensus values. Both observations indicate the nano-EA measurements are comparable to conventional EA analyses and accurately reproduce the VPDB and AIR isotopic scales. Critical to the success of the nano-EA system is the procedure for removing the blank contribution to the measured values. Statistical treatment of uncertainties for this procedure yields an accurate method for calculating internal and external precision.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".