An improved setup for radiocarbon analysis of water-soluble organic carbon in environmental matrices
Bibliographic record
Abstract
Abstract This paper presents an improved setup for radiocarbon analysis of water-soluble organic carbon based on wet chemical oxidation as installed at the Laboratory for the Analysis of Radiocarbon with AMS (LARA) at the University of Bern. The implementation of a non-dispersive infrared CO2 detector allows more precise and accurate quantification of carbon amounts in samples and establishes the possibility of simple monitoring of the efficacy of flushing and sampling processes. A detailed blank assessment unveiled undesired oxidation of different materials and sample temperature as critical factors regarding the level of constant contamination. Contamination arising from oxidation of septum pieces and carbon-based glues in conventional sampling needles was minimized by developing a glass-sintered needle. This new needle was also designed to be longer, reducing the minimum amount of sample solution needed to 2 mL. The oxidation time and temperature (1 hr at 75°C) were optimized to further decrease contamination during analyses of samples with carbon amounts of up to ∼50 µg. With these improvements, we now report low constant contamination levels of 0.62 ± 0.12 µg C (with F14C of 0.19 ± 0.04), whereas the cross contamination factor was determined to be 0.25 ± 0.07%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".