On-Line High-Precision Stable Hydrogen Isotopic Analyses on Nanoliter Water Samples
Bibliographic record
Abstract
We describe a new on-line chromium reduction technique for the measurement of stable hydrogen (deltaD) isotopes in waters using continuous-flow isotope ratio mass spectrometry. The on-line Cr reduction method has low intersample memory effects (< 1%) and excellent precision and accuracy for deltaD (+/-0.5% and was used to analyze waters samples as small as 50 nL. The on-line Cr method has a number of significant advantages over conventional offline Zn and U reduction and on-line carbon-based pyrolysis techniques. A single Cr reactor can be used to analyze approximately 1,000 water samples using an injection volume of 0.5 microL, with an individual sample analysis time of 4 min. Intersample memory effects are negligible. The Cr reactor temperature of 1050 degree C is easily attainable on standard elemental analyzers and so does not require the specialized and costly high-temperature furnaces of carbon-based pyrolysis reactors. Furthermore, hydrogen isotopes in extremely small water samples in the 100-nL range or less can be easily measured; hence, this new method opens up a number of exciting application areas in earth and environmental sciences, for example, natural abundance deltaD measurements of individual fluid inclusions in geologic materials using a laser source and measurements of body fluids in physiological and metabolic research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".