Bibliographic record
Abstract
Abstract Bioremediation, which exploits biological processes to remove contaminants from the biosphere, is generally a low‐cost option among remediation alternatives. The organisms involved are predominantly microbes which have the capacity to survive contaminant toxicity and access and transform the contaminants. Many contaminants are organic solvents which disrupt membranes, and cells may develop defence mechanisms including formation of outer cell‐membrane‐protective material, often hydrophobic or solvent efflux pumps. Many of the contaminants are hydrophobic and microbial species appear to take up these substances by a combination of biosurfactant secretion and direct cell‐contaminant association. Bioremediation may involve aerobic or anaerobic microbial activities. Aerobic degradation often involves introduction of oxygen atoms in reactions mediated by monooxygenases, dioxygenases, hydroxylases, oxidative dehalogenases, or chemically reactive oxygen atoms generated by enzymes such as ligninases or peroxidases. Anaerobic degradations of predominant contaminants such as petroleum hydrocarbons, typically involve initial activation reactions followed by oxidative catabolism mediated by anoxic electron acceptors. Many microbes participate in bioremediation processes by mineralizing the organic contaminants to end‐products such as carbon dioxide and water, or to metabolic intermediates which are used as primary substrates for cell growth. Cells may be able to biotransform the contaminant without being able to utilize the contaminant for growth in a process known as cometabolism. Microbes have also evolved biocatalytic systems which indirectly mediate the transformation of some recalcitrant molecular species, mediated by enzymes which generate chemically reactive species which in turn, attack the recalcitrant molecules. Major strategies for implementing bioremediation processes include biostimulation and bioaugmentation approaches. Biostimulation involves adding supplements to a contaminated site with the objective of stimulating growth of the microbial population already present there, which may be capable of degrading the contaminants. Where the contaminated environment does not contain microbes capable of degrading the contaminants, there may be a case for addition of a selected and acclimated microbial inoculum. This is termed bioaugmentation. Widely used process configurations include slurry bioreactors, landfarming, soil‐pile composting systems, engineered soil piles, in situ subsurface bioremediation and phytoremediation. Bioremediation accounts only for a small fraction of the remediation market because arguably, some of the processes implemented failed to meet the required contaminant reduction criteria. For bioremediation processes to gain access to a more substantial proportion of the remediation market, processes must be judiciously selected to be technically dependable and cost‐effective.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 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; both teacher heads agree on what is shown here.
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".