A new UV-Oxidation set up for AMS radiocarbon analysis for small dissolved organic carbon in marine and fresh water samples
Bibliographic record
Abstract
Radiocarbon measurements of dissolved organic carbon (DOC) can give us valuable information about origin and age of DOC, a major, yet little understood component in the global carbon cycle. One way to measure DOC in water is to remove dissolved inorganic carbon first, oxidize organic carbon with UV irradiation and ultimately analyses the formed inorganic CO2 for 14C. The main challenge of UV-Oxidation (UVox) methods is to extract the typically low concentrations of DOC with low blanks required for relatively high precision 14C measurements. A disadvantage of currently used UVox methods is that only one sample can be oxidized in a laborious process at the same time in large volume. Here we present a UV-Oxidation system where up to 12 water samples can be oxidized simultaneously in 12 separate quartz reactors arranged around a single UV lamp in a compact setup. The simple setup further uses helium instead of vacuum typically used by conventional extraction lines to speed up the extraction of the formed CO2 after oxidation. The key improvements of the new UVox setup are: 1) Reduced amount of water needed (30- 60 ml) as samples are measured for 14C with the Micadas gas ion source., 2) UV oxidation efficiency for standards is high (96%), 3) No KI trap is needed, 4) Required time for sample preparation of up to 12 samples is 4-6 h, 5) combined the CO2 from different reactors to one trap. We obtained 2.6 ± 0.6 µgC with F14C= 0.27±0.05 for processing blank. We will present the first measurments of DOC samples from Swiss lakes, Canadian Beaufort Sea and the reproducibility of the line.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".