Physical controls on the isotopic composition of soil‐respired CO<sub>2</sub>
Bibliographic record
Abstract
Measurement of the isotopic composition of soil and soil‐respired CO2 (δ13CO2) has become an invaluable tool in understanding ecosystem carbon‐cycling processes. While steady state work has been indispensable in understanding the effects of diffusive transport on soil CO2 isotopic composition, it is crucial that researchers studying temporally dependent processes, such as soil CO2 efflux, realize that these systems are rarely at steady state. Non‐steady‐state effects could result in misinterpretation of isotopic data, but have not been addressed in the literature, despite their fundamental importance to researchers who use isotopes in diffusive, non‐steady‐state environments. Here, we use an isotopologue‐based model to study dynamic fractionation, which we propose is a byproduct of transient changes in environmental variables. Time varying soil characteristics and processes such as biological production rate, soil pore space, diffusivity and atmospheric concentration were all found to induce non‐steady‐state gas transport conditions in the soil leading to transient changes in the isotopic composition of soil CO2 flux. The main driving force behind this transport related fractionation of CO2 is the rate of the change in 12CO2 gradient compared to that of 13CO2. These numerical simulations show that dynamic fractionation exists under non‐steady‐state diffusive conditions and suggest that isotopic data collected in non‐steady‐state, natural environments, cannot be properly interpreted without considering dynamic fractionation effects.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".