Technical Note: Evaluation of between-sample memory effects in the analysis of δ <sup>2</sup> H and δ <sup>18</sup> O of water samples measured by laser spectroscopes
Bibliographic record
Abstract
Abstract. This study evaluated between-sample memory in isotopic measurements of δ2H and δ18O in water samples by laser spectroscopy. Ten isotopically depleted water samples spanning a broad range of oxygen and hydrogen isotopic compositions were measured by three generations of off-axis integrated cavity output spectroscopy and cavity ring-down spectroscopy instruments. The analysis procedure encompassed small (less than 2‰ for δ2H and 1‰ for δ18O) and large (up to 201‰ for δ2H and 25‰ for δ18O) differences in isotopic compositions between adjacent sample vials. Samples were injected 18 times each, and the between-sample memory effect was quantified for each analysis run. Results showed that samples adversely affected by between-sample isotopic differences stabilised after seven–eight injections. The between-sample memory effect ranged from 14% and 9% for δ2H and δ18O measurements, respectively, but declined to negligible carryover (between 0.1% and 0.3% for both isotopes) when the first ten injections of each sample were discarded. The measurement variability (range and standard deviation) was strongly dependent on the isotopic difference between adjacent vials. Standard deviations were up to 7.5‰ for δ2H and 0.54‰ for δ18O when all injections were retained in the computation of the reportable δ-value, but a significant increase in measurement precision (standard deviation in the range 0.1‰–1.0‰ for δ2H and 0.05‰–0.17‰ for δ18O) was obtained when the first eight injections were discarded. In conclusion, this study provided a practical solution to mitigate between-sample memory effects in the isotopic analysis of water samples by laser spectroscopy.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".