Bioaccessible Porosity in Soil Aggregates and Implications for Biodegradation of High Molecular Weight Petroleum Compounds
Bibliographic record
Abstract
We evaluated the role of soil aggregate pore size on biodegradation of essentially insoluble petroleum hydrocarbons that are biodegraded primarily at the oil-water interface. The size and spatial distribution of pores in aggregates sampled from biodegradation experiments of a clayey, aggregated, hydrocarbon-contaminated soil with relatively high bioremediation end point were characterized by image analyses of X-ray micro-CT scans and N2 adsorption. To determine the bioaccessible pore sizes, we performed separate experiments to assess the ability of hydrocarbon degrading bacteria isolated from the soil to pass through membranes with specific sized pores and to access hexadecane (model insoluble hydrocarbon). Hexadecane biodegradation occurred only when pores were 5 μm or larger, and did not occur when pores were 3 μm and smaller. In clayey aggregates, ∼ 25% of the aggregate volume was attributed to pores larger than 4 μm, which was comparable to that in aggregates from a sandy, hydrocarbon-contaminated soil (~23%) scanned for comparison. The ratio of volumes of inaccessible pores (<4 μm) to bioaccessible pores (>4 μm) in the clayey aggregates was 0.32, whereas in the sandy aggregates it was approximately 10 times lower. The role of soil microstructure on attainable bioremediation end points could be qualitatively assessed in various soils by the aggregate characterization approach outlined herein.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".