Lateral diffusion and atmospheric CO<sub>2</sub> mixing compromise estimates of rhizosphere respiration in a forest soil
Bibliographic record
Abstract
Measurements of rhizosphere carbon efflux are critical to the determination of soil carbon balance by CO2 flux measurements. We attempted to measure rhizosphere respiration in a forest ecosystem by transplanting 13C-enriched soils from a tallgrass prairie into a mixed-conifer forest soil but found that atmospheric air mixing and lateral diffusion confounded delta13C-CO2 measurements. Surface CO2 efflux (delta13C [Formula: see text] 20) was enriched 6 relative to soil CO2 measured at depth because of the presence of atmospheric-derived CO2 (8) near the soil surface. The delta13C-CO2 value of transplanted soil CO2 did not reflect its 13C-enriched carbon source but was within 1 of native soil CO2 because of lateral diffusion from the surrounding native soil. A two-component steady-state model of lateral diffusion supported our assertion that this soil was susceptible to atmospheric air mixing and lateral diffusion because of its high effective porosity and relatively low concentration of soil CO2. Percent rhizosphere respiration was estimated at 35 and 45% after applying corrections for atmospheric air mixing and (or) lateral diffusion. These confounding effects may be reduced or eliminated by utilizing a larger transplanted soil pit and by reducing soil CO2 diffusivity, for example, by increasing water content.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".