Oxygen isotope measurements of seawater (<sup>18</sup>O/<sup>16</sup>O): A comparison of cavity ring‐down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS)
Bibliographic record
Abstract
Abstract Laser‐based spectroscopic techniques, such as cavity ring‐down spectroscopy (CRDS), provide a new, cost effective and more widely available approach to measure the oxygen isotope ratio in water molecules, 18O/ 16O (δ18O), and are used increasingly to measure δ18O in the world's oceans. Here, we present results from an interlaboratory comparison designed to evaluate the quality of CRDS‐derived measurements, and their consistency with values measured by isotope ratio mass spectrometry (IRMS). We also discuss the influence of salt on instrument performance and sample throughput for the analysis of seawater samples. This study compared measurements of δ18O from natural samples with a wide range of salinities (0, 29.4, and 34.6) performed by four independent labs: two using CRDS and two using IRMS. We also compared δ18O measurements of Northeast Atlantic Deep Water collected in 2013, 2012, 2009, and 1995 from the AR7W repeat hydrography transect across the Labrador Sea. The within‐lab precision of ocean‐based CRDS measurements is seen to approach 0.03‰, which is better than the manufacturer's typically stated analytical precision (around +/− 0.05‰), and comparable to that achievable with IRMS. The interlaboratory differences of measurements (highest‐lowest) reported by the four labs is taken as an indicator of overall accuracy, and is estimated conservatively as being < 0.1‰, with the potential to approach 0.05‰. Overall, these results show that CRDS based 18O measurements of seawater can be equivalent to high‐quality measurements by IRMS.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".