Carbon transformations by indigenous microbes in four hydrocarbon-contaminated soils under static remediation conditions
Bibliographic record
Abstract
We sought to learn about the transformations of hydrocarbons and limitations to bioremediation in four hydrocarbon-contaminated soils. Two soils were contaminated with creosote and two with petroleum. We incubated them either with or without added N, P, K and S. We monitored CO2 evolution, and residual dichloromethane-extractable organic C (DEO-C) after 10 wk. Indigenous populations were active in all soils. A single-component first-order model fit the CO2 respiration rate data, yielding estimates of potentially mineralizable C (Co), and specific decay rate, k. The ratio C: DEO was lower in heavier textured and strongly aggregated soils compared with the more poorly aggregated sandy soils. Low respiration rates in the more clayey soils were related to low Co rather than to k for the available C. In the highly amended soils the loss of total C approximated the production of CO2-C while the loss of DEO-C was greater than the evolution of CO2-C. We conclude: 1) Under circumstances such as hydrocarbon contaminants with long exposure to the soil, static systems may be sufficient for metabolism of available contaminants by indigenous microorganisms. 2) Increases in clay content and stability of aggregates, together with biotreatment to remove hydrocarbons may reduce bioavailability of residual contamination. 3) In soils with high clay content, contaminant transformations or attenuation without production of CO2 may be substantial. Key words: Bioremediation, bioaugmentation, soil, hydrocarbons, contaminant, kinetic models, bioavailability, respiration
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".