Isotopic fractionation corrections for the radiocarbon composition ofCO <sub>2</sub> in the soil gas environment must include diffusion and mixing
Bibliographic record
Abstract
Abstract. Earth system scientists working with radiocarbon in organic samples use a stable carbon isotope (δ13C) correction to account for mass-dependent fractionation caused primarily by photosynthesis. Although researchers apply this correction routinely, it has not been evaluated for the soil gas environment, where both diffusive gas transport and diffusive mixing are important. Towards this end we applied an analytical soil gas transport model across a range of soil diffusivities and biological CO2 production rates, allowing us to control the radiocarbon (Δ14C) and stable isotope (δ13C) compositions of modeled soil CO2 production and atmospheric CO2. This approach allowed us to assess the bias that results from using the conventional correction method for estimating Δ14C of soil production. We found that the conventional correction is inappropriate for interpreting the radio-isotopic composition of CO2 from biological production, because it does not account for diffusion and diffusive mixing. The resultant Δ14C bias associated with the traditional correction is highest (up to 150 ‰) in soils with low biological production and/or high soil diffusion rates. We propose a new solution for radiocarbon applications in the soil gas environment that fully accounts for diffusion and diffusive mixing.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".