The Charosphere Promotes Mineralization of <sup>13</sup>C‐Phenanthrene by Psychrotrophic Microorganisms in Greenland Soils
Bibliographic record
Abstract
When soil is frozen, biochar promotes petroleum hydrocarbon (PHC) degradation, yet we still do not understand why. To investigate microbial biodegradation activity under frozen conditions, we placed 60-μm mesh bags containing 6% (v/v) biochar created from fishmeal, bonemeal, bone chip, or wood into PHC-contaminated soil, which was then frozen to -5°C. This created three soil niches: biochar particles, the charosphere (biochar-contiguous soil), and bulk soil outside of the bags. After 90 d, C-phenanthrene mineralization reached 55% in bonemeal biochar and 84% in bone chip biochar charosphere soil, compared with only 43% in bulk soil and 13% in bone chip biochar particles. Soil pH remained near neutral in bone chip and bonemeal biochar treatments, unlike wood biochar, which increased alkalinity and likely made phosphate unavailable for microorganisms. Generally, charosphere soil had higher aromatic degradative gene abundances than bulk soil, but gene abundance was not directly linked to C-phenanthrene mineralization. In bone chip biochar-amended soils, phosphate successfully predicted microbial community composition, and abundances of and increased in charosphere soil. Biochar effects on charosphere soil were dependent on feedstock material and suggest that optimizing the charosphere in bone-derived biochars may increase remediation success in northern regions.
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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".