Adapting Automatic Water Samplers for the Isotopic Study of Rainfall at High Temporal Resolution
Bibliographic record
Abstract
RATIONALE: Stable water isotopes in precipitation are powerful tracers of atmospheric processes. Automatic rain samplers are valuable for high temporal resolution isotopic studies but building them from scratch requires significant financial and material resources. A commercial water autosampler has been modified to prevent post-sampling evaporation and to allow for intra-event precipitation sampling. METHODS: New sampling bottles were created by reducing the original volume and opening area. Evaporation tests were carried out on different volumes of water for 72 h under laboratory conditions. These were used to determine the minimum amount of rain to collect to minimize the impact of isotopic fractionation by evaporation. The impact of the autosampler's air moisture saturation was also tested. Samples were analyzed by dual-inlet isotope ratio mass spectrometry and cavity ring-down spectroscopy. RESULTS: For samples larger than 10 mL, evaporative heavy isotope enrichment due to Rayleigh distillation remains negligible compared with the overall analytical uncertainty. Intentional saturation of the autosampler's atmosphere significantly reduces post-sampling evaporation but leads to equilibration of the samples with the added water. We have investigated the maximum time that samples must be left for this fractionation to remain negligible. Under these conditions, this autosampler is suitable for intra-event rainfall sampling for isotopic analysis. CONCLUSIONS: It is now possible to perform low-cost high-resolution precipitation sampling for isotopic analysis. The intentional air saturation of the sampler, which effectively prevents post-sampling evaporation, had never been proposed before. All instructions for modifying this sampler are now available in open access so the scientific community can easily repeat them.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".