A Transient Model of Vadose Zone Reaction Rates Using Oxygen Isotopes and Carbon Dioxide
Bibliographic record
Abstract
The importance of identifying and quantifying subsurface geochemical reaction rates and processes by monitoring and modeling CO 2 and O 2 concentrations is well established. These parameters, however, are typically studied independently under presumed steady‐state conditions. Here we present models of seasonally variable vadose zone CO 2 and O 2 concentrations that use δ 18 O of O 2 as a constraint to create a dynamic link between these three parameters under transient conditions. The gas transport modeling was used to quantify the controls of biogeochemical processes and parameters (i.e., temperature and moisture content) on vadose zone distributions of CO 2 and O 2 gas concentrations. The investigation was conducted on a 3‐m‐thick, unvegetated, fine‐sand vadose zone located in northern Alberta, Canada (56°40′N, 111°07′W). Using the modeled molar ratio of surface fluxes for O 2 and CO 2 , the change in reaction rate for a temperature change of 10°C ( Q 10 ), moisture content at maximum reaction rates, and biogeochemical discrimination against consumption of 18 O 16 O (α k ), we determined that organic C oxidation by microbial respiration was the predominant mechanism consuming O 2 and producing CO 2 The mean α k was determined to be 0.973, suggesting that subsurface respiration was via the alternative oxidase pathway, which may be common in cold climates. Modeling revealed that the moisture content of a moist, surficial clayey sand layer (0.1–0.3 m thick) had a dramatic effect on pore‐gas CO 2 and O 2 concentrations and on δ 18 O O2 The vadose zone in this study was at an unvegetated site to simplify the model application; however, it can be modified to include root respiration and applied to natural vadose zones to help quantify the role of subsurface respiration in global O 2 and C budgets.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".