Microbial communities of upland peat swamps were no different 1 year after a hazard reduction burn
Bibliographic record
Abstract
Fire in wetlands is poorly understood, yet hazard reduction burns are a common management practice and bushfires are becoming increasingly prevalent because of climate change. Fire may have long-lasting implications for the microbial component of these wetland ecosystems that regulate carbon and nutrient cycling. The extremely fire-prone Blue Mountains World Heritage Area in south-eastern Australia contains hundreds of endangered peat-forming upland swamps that regularly experience both bushfires and hazard reduction burns. In a before–after control–impact study, we surveyed the sediment microbial community of these swamps to test the impact of a low-intensity hazard reduction burn. Along with sediment pH, moisture and organic content, we measured gene abundances including those relating to carbon cycling (quantitative PCR (qPCR) of pmoA, mcrA, bacterial 16S rRNA and archaeal 16S rRNA), and bacteria community fingerprint (terminal restriction fragment length polymorphism (T-RFLP)). One year after the hazard reduction burn, there were no significant differences in the gene abundances or microbial community fingerprint that could be attributed to the fire, suggesting that the hazard reduction burn did not have a long-term impact on these 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 | 0.000 |
| 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) | 0.001 | 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".