Burn-induced decreases in soil microbial carbon use efficiency vary across soil types and substrates
Bibliographic record
Abstract
Abstract Wildfires cause immediate changes in above and belowground carbon (C) stocks in boreal forest ecosystems with long-term repercussions for C cycling. Understanding the role of soil microbes in mediating post-fire C cycling and recovery is an important step to predicting how these ecosystems will respond to novel wildfire regimes caused by climate change. Wildfires can cause large shifts in soil bacterial and fungal community composition that can persist for years post-fire. Less is known about the effects of fire on soil microbial community function, such as C use efficiency (CUE). In this study, we measured the effects of burning on substrate-specific CUE using a laboratory incubation of boreal forest soils. We amended burned and unburned soils with either 13 C-labelled ground pine roots or glucose and measured the amount of added substrate C that was incorporated into microbial biomass C versus respired as CO 2 in order to calculate CUE. Burning caused a decrease in the amount of soil microbial biomass and respiration derived from soil organic C. Glucose-specific CUE declined with burning, driven by a decrease in glucose-derived microbial biomass. This decrease in glucose-specific CUE following burning correlated with an increase in weighted mean predicted 16S rRNA gene copy number, raising the possibility of using copy number as a proxy for post-fire CUE in boreal forest soils. Overall, pine-specific CUE was lower than glucose-specific CUE, likely reflecting the difference in chemical complexity between the two substrates; burning had a much smaller effect on pine-specific CUE, highlighting the variability of CUE between substrates in burned soils.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".