Affordable event and monthly rain samplers: Improving isotopic datasets to understand meteorological processes
Bibliographic record
Abstract
RATIONALE: Water-stable isotopes in rainfall are powerful tracers of atmospheric processes at different spatial and temporal scales. However, commercially available rain samplers for isotopic analysis are prohibitively expensive, especially for high spatial resolution networks and studies conducted in developing countries. A low-cost, simple, and robust sampler was designed for event and monthly rainfall samplings. METHODS: Rainfall collectors were built based on existing designs provided in the literature and using easily accessible materials. Event samplers were filled with different volumes of reference water and left for 72 h in laboratory conditions to determine the minimum amount of rainfall to be collected to minimize isotopic fractionation, from both postsampling evaporation and equilibration. Samples were analyzed using dual-inlet isotope ratio mass spectrometry and cavity ring-down spectroscopy. RESULTS: For samples larger than 4% of the bottle's capacity, the evaporative enrichment due to Rayleigh distillation is negligible compared to the overall analytical uncertainty. Using a tube connecting the funnel to the water sample has proved to reduce postsampling evaporation by at least five times. To limit water self-diffusion, we recommend collecting the largest rainfall amount possible. Under these conditions, these collectors are suitable for rainfall sampling for isotopic analysis. CONCLUSIONS: This low-cost methodology will enable isotopic sampling of precipitation at high spatial resolutions and democratize the use of isotopes for hydrological studies in developing countries. All instructions for building and using these samplers are made openly accessible to the scientific community so they can be repeated and adapted to the needs of each project.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".