Optimizing measurement of soil potential high‐affinity H <sub>2</sub> uptake activity across pH
Bibliographic record
Abstract
Abstract Soil high‐affinity H 2 uptake activity can be affected by many factors, including the soil pH. However, the method to determine how pH affects high‐affinity H 2 uptake activity should be updated. The effect of pH on the biological high‐affinity H 2 uptake in agricultural soils was compared using three pH buffer systems in the pH 4–8 range. Soil pH was adjusted to the target pH using a buffer system (1 g soil/5 mL pH buffer). Soil slurries were treated with heat (autoclaving) or a chemical (25% v/w of toluene addition, microbial inhibitor) to inhibit biological activity. Sterile pH buffer was used as a negative control. The sterile soil slurry (heat sterilization) was the optimal reference control for measuring biological high‐affinity H 2 uptake activity. Biological H 2 uptake activity was resistant to toluene, particularly at extreme pH levels. Overall, soil pH ( p = 0.95) and pH buffer systems ( p = 0.46) did not affect the high‐affinity H 2 uptake activity in the tested agricultural soils. We provide an updated method to accurately measure the potential high‐affinity H 2 uptake activity in soil, with an emphasis on the importance of controlling the soil pH.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".