Bibliographic record
Abstract
Abstract. High temporal and spatial resolution water sampling of stream water or precipitation for subsequent stable water isotope analysis (δ2H and δ18O) is commonly conducted with self-made or commercial automated samplers. However, prolonged storage of water samples in open bottles inside autosamplers can lead to isotopic alterations caused by evaporative fractionation and vapor mixing. In this study, we tested the effectiveness of different evaporation barriers—dip-in tubes, ball valves, and siphons—under controlled laboratory conditions. Experiments were conducted using glass bottles with varying filling levels (50 mL to 250 mL in 250 mL bottles) and exposure times of up to 52 days. Our results demonstrate that all tested barriers effectively reduce isotopic alterations. Among these, the dip-in tube emerged as the simplest and most efficient solution, reliably preserving isotopic data even over extended storage durations. We also observed isotopic differences between the water in the dip-in tube and the main bottle. This phenomenon becomes particularly relevant when sample volumes are very low, as water from the dip-in tube may influence the overall isotopic composition of the sample. This finding highlights the need for careful consideration of bottle design, funnel size and sample handling, especially with low water volumes. These findings provide valuable insights for the design of cost-effective, automated water sampling systems for stable isotope applications, emphasizing the importance of evaporation barriers to ensure reliable and accurate isotopic analyses.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.028 | 0.026 |
| Insufficient payload (model declined to judge) | 0.161 | 0.129 |
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".