High-Precision Laser Spectroscopy D/H and <sup>18</sup>O/<sup>16</sup>O Measurements of Microliter Natural Water Samples
Bibliographic record
Abstract
Newly available gas analyzers based on off-axis integrated cavity output spectroscopy (OA-ICOS) lasers have been advocated as an alternative to conventional isotope-ratio mass spectrometers (IRMS) for the stable isotopic analysis of water samples. In the case of H2O, OA-ICOS is attractive because it has comparatively low capital and maintenance costs, the instrument is small and field laboratory portable, and provides simultaneous D/H and 16O/18O ratio measurements directly on H2O molecules with no conversion of H2O to H2, CO, or H2/CO2-water equilibration required. Here we present a detailed assessment of the performance of a liquid-water isotope analyzer, including instrument precision, estimates of sample memory and sample mass effects, and instrumental drift. We provide a recommended analysis procedure to achieve optimum results using OA-ICOS. Our results show that, by using a systematic sample analysis and data normalization procedure routine, measurement accuracies of +/-0.8 per thousand for deltaD and +/-0.1 per thousand delta18O are achievable on nanoliter water samples. This is equivalent or better than current IRMS-based methods and at a comparable sample throughput rate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".