Bioremediation- A Beneficial Use of Biotechnology and Allied Engineering Technologies in Oil Producing Regions
Bibliographic record
Abstract
Microbial decomposition has played a major role in the sustenance of societies by decomposing various materials thus enhancing physical space on the earth. However, a continuous increase in world population now threatens the carrying capacity of our planet. Within a defined space on Earth, the ever increasing number of living organisms creates challenges for the management of waste materials generated. Identification and isolation of microbes capable of decomposing wastes and hazardous materials and discovery of processes to produce genetically engineered microorganisms (GEMS) through biotechnological processes give hope to making this world a better place to live by managing global wastes using tamed microorganisms. Scientists coin this process of utilizing GEMS with defined characteristics of decontaminating waste and harmful materials as bioremediation. In this study, we evaluate relative scientific data from bioremediation and oil spills in the Middle East. The US National Library of Medicine, National Institutes of Health, PubMed and PubMed Health databases were used for retrieving information relevant to application of bioremediation in oil spills and issues associated with oil contamination in peripheries of oil drilling areas. The size of these databases gradually increase, however, based on our access in the last week of October 2015, the word “bioremediation” search on the PubMed databases returned above 36,000 articles, and among these a combined search of “bioremediation AND Oil” returned 2,400 articles which shows that 7% of global research in the area of bioremediation is dedicated towards oil cleaning. A further refining of the search in reference to Middle East (bioremediation AND Oil AND Middle East) returns 41 articles thus revealing 1.7% of the research activities occur in the Middle East. According to the US Energy Information Administration, the Middle East harbors 49% reserves of crude oil, whereas, it shares a significantly weaker output as far as scientific contributions remain. Based on this information, we provide our analyses of the ongoing bioremediation in general with a particular focus on oil and propose strengthening of biotechnology programs in collaboration with the discipline of engineering to foster research in oil bioremediation in the Middle East
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".