The microbiomes and metagenomes of forest biochars
Bibliographic record
Abstract
Biochar particles have been hypothesized to provide unique microhabitats for a portion of the soil microbial community, but few studies have systematically compared biochar communities to bulk soil communities. Here, we used a combination of sequencing techniques to assess the taxonomic and functional characteristics of microbial communities in four-year-old biochar particles and in adjacent soils across three forest environments. Though effects varied between sites, the microbial community living in and around the biochar particles had significantly lower prokaryotic diversity and higher eukaryotic diversity than the surrounding soil. In particular, the biochar bacterial community had proportionally lower abundance of Acidobacteria, Planctomycetes, and β-Proteobacteria taxa, compared to the soil, while the eukaryotic biochar community had an 11% higher contribution of protists belonging to the Aveolata superphylum. Additionally, we were unable to detect a consistent biochar effect on the genetic functional potential of these microbial communities for the subset of the genetic data for which we were able to assign functions through MG-RAST. Overall, these results show that while biochar particles did select for a unique subset of the biota found in adjacent soils, effects on the microbial genetic functional potential appeared to be specific to contrasting forest soil environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".