Automation of <sup>13</sup>C/<sup>12</sup>C ratio measurement for freshwater and seawater DOC using high temperature combustion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We provide a detailed description of the hyphenation of an Aurora 1030C high temperature catalytic conversion DOC analyzer, a GD‐100 CO 2 trap and a continuous flow IRMS, which has made possible the high‐throughput, automated measurements of 13 C/ 12 C ratios, and DOC concentrations for a wide range of aquatic samples. Precision of 13 C/ 12 C 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 δ 13 C, our two laboratories analyzed a consensus reference material from a deep‐ocean environment (CRM Batch 13 Lot # 05‐13, Hansell 2013) and found δ 13 C 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it