Extraneous Carbon Assessments in Radiocarbon Measurements of Black Carbon in Environmental Matrices
Bibliographic record
Abstract
Extraneous carbon (Cex) added during chemical processing and isolation of black carbon (BC) in environmental matrices was quantified to assess its impact on compound specific radiocarbon analysis (CSRA). Extraneous carbon is added during the multiple steps of BC extraction, such as incomplete removal of solvents, and carbon bleed from the gas chromatographic and cation columns. We use 2 methods to evaluate the size and Δ14C values of Cex in BC in ocean sediments that require additional pretreatment using a cation column with the benzene polycarboxylic acid (BPCA) method. First, the direct method evaluates the size and Δ14C value of Cex directly from the process blank, generated by processing initially empty vials through the entire method identically to the treatment of a sample. Second, the indirect method quantifies Cex as the difference between processed and unprocessed (bulk) Δ14C values in a variety of modern and 14C-free or “dead” BC standards. Considering a suite of hypothetical marine sedimentary samples of various sizes and Δ14C values and BC Ring Trial standards, we compare both methods of corrections and find agreement between samples that are >50 μg C. Because Cex can profoundly influence the measured Δ14C value of compound specific samples, we strongly advocate the use of multiple types of process standards that match the sample size to assess Cex and investigate corrections throughout extensive sample processing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".