Bibliographic record
Abstract
Priming of soil organic matter (SOM) decomposition by microorganisms is a key phenomenon of global carbon (C) cycling. Soil pH is a main factor defining priming effects (PEs) because it (i) controls microbial community composition and activities, including enzyme activities, (ii) defines SOM stabilization and destabilization mechanisms, and (iii) regulates intensities of many biogeochemical processes. In this critical review, we focus on prerequisites and mechanisms of PE depending on pH and assess the global change consequences for PE. The highest PEs were common in soils with pH between 5.5 and 7.5, whereas low molecular weight organic compounds triggered PE mainly in slightly acidic soils. Positive PEs up to 20 times of SOM decomposition before C input were common at pH around 6.5. Negative PEs were common at soil pH below 4.5 or above 7 reflecting a suboptimal environment for microorganisms and specific SOM stabilization mechanisms at low and high pH. Short-term soil acidification (in rhizosphere, after fertilizer application) affects PE by: mineral-SOM complexation, SOM oxidation by iron reduction, enzymatic depolymerization, and pH-dependent changes in nutrient availability. Biological processes of microbial metabolism shift over the short-term, whereas long-term microbial community adaptations to slow acidification are common. The nitrogen fertilization induced soil acidification and land use intensification strongly decrease pH and thus boost the PE. Concluding, soil pH is one of the strongest but up to now disregarded factors of PE, defining SOM decomposition through short-term metabolic adaptation of microbial groups and long-term shift of microbial communities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".