Automation of <sup>13</sup>C/<sup>12</sup>C ratio measurement for freshwater and seawater DOC using high temperature combustion
Bibliographic record
Abstract
We provide a detailed description of the hyphenation of an Aurora 1030C high temperature catalytic conversion DOC analyzer, a GD‐100 CO2 trap and a continuous flow IRMS, which has made possible the high‐throughput, automated measurements of 13C/12C ratios, and DOC concentrations for a wide range of aquatic samples. Precision of 13C/12C ratios increases exponentially with sample concentration, reaching 0.2‰ or better for high concentration samples (>5 mg L‐1), comparable to that obtained in a conventional elemental analyzer‐IRMS setup. The system blank contribution is the limiting factor in obtaining maximal performance; optimal system blanks values are on the order of 0.2 µg C with an isotopic signature varying from ‐20 to ‐12‰ during the lifetime of the combustion column. With appropriate blank correction procedures, accurate analyses (±0.5‰ or better) can be obtained on concentrations as low as 0.5 mg DOC L‐1, representing the lower limit typically observed in aquatic systems. Sample matrix does not affect reproducibility or accuracy; this method is amenable to both freshwater and seawater samples. Although no certified DOC standards exist for δ13C, our two laboratories analyzed a consensus reference material from a deep‐ocean environment (CRM Batch 13 Lot # 05‐13, Hansell 2013) and found δ13C values of ‐19.9 ± 0.5‰ (n = 4) and ‐20.6 ± 0.3‰ (n = 3), which corroborates previously reported values for similar samples (Bouillon et al. 2006; Lang et al. 2007; Panetta et al. 2008) and is consistent with its marine origin.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".