Bibliographic record
Abstract
Bioremediation is a viable mechanism for treating soils contaminated with petroleum hydrocarbons. Bioremediation strategies range from encouraging natural biodegradation processes (biostimulation) to supplementing the existing system with microorganisms able to degrade the contamination (bioaugmentation) and to monitoring and verifying natural processes (natural attenuation). Application of bioremediation technologies is customized to specific site characteristics, as contaminated soils may be excavated for on- or off-site treatment at surface (ex situ) or treated in place (in situ). In situ technologies, such as bioventing, are often cost-effective, but delivery and mixing of stimulants with the microorganisms and contaminants are challenging. Ex situ technologies, namely, landfarming and biopiles, provide greater process control but also increase costs, disruption, and exposure to contaminants. Microorganisms capable of degrading petroleum hydrocarbons have been found to be prolific in the subsurface. Alteration of environmental conditions is often paramount to enabling biodegradation; temperature, pH, salinity, nutrients, moisture, and redox condition may be altered to enhance or accelerate treatment. Biosurfactants also provide an additional means of improving treatment by increasing the surface area of hydrophobic hydrocarbon compounds, thus increasing exposure to microorganisms. Bioremediation of soil contaminated with petroleum hydrocarbons provides a flexible, cost-effective, environmentally sustainable treatment strategy that is tailored to site-specific conditions and requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".