Editorial: Petroleum Microbial Biotechnology: Challenges and Prospects
Bibliographic record
Abstract
It has become evident that fossil fuels such as petroleum will continue to contribute a major fraction of the energy portfolio worldwide for the coming decades.The United States Energy Information Administration anticipates a growth of global oil demand up to 123 million barrels per day by 2025(Sahu et al., 2015)).Accordingly, oil production and processing operations are expanding continuously to meet the accelerated growth in global energy demand.Unfortunately, serious environmental pollution issues are associated with petroleum recovery, transportation, and refining.In addition, these processes are energy-intensive, costly, and, in some cases, not sufficiently efficient (Gray, 1994;Kilbane, 2006;Ramirez-Corredores and Borole, 2006).Furthermore, the increased demand for fossil fuels will inevitably force the oil industry to produce and refine increasing amounts of unconventional resources such as heavy and extra-heavy crudes as well as bitumen.This will lead to even more environmental issues in addition to technical challenges for the oil industry (Ramirez-Corredores and Borole, 2006;Speight, 2013).The continuously rising global demand for cleaner fuels, together with the depletion of light crude oil resources and strict environmental regulations, have provoked the need for alternative or complementary novel technologies for oil production and refining.The interaction between microorganisms and petroleum hydrocarbons has been well recognized and the intimate contact between them starts in oil-bearing subsurface formations (Ehrlich et al., 2016).This constituted the basis from which petroleum biotechnology has emerged.Petroleum biotechnology exploits the astonishing metabolic and adaptive capabilities of dedicated hydrocarbon-degrading/transforming microorganisms (Van Hamme et al., 2003;Mbadinga et al., 2011).As compared to conventional thermochemical and physical approaches, biotechnologybased processes are generally environmentally friendly, economic, and are characterized by high selectivity (Le Borgne and Quintero, 2003;Kilbane, 2006).Petroleum biotechnology has been applied for environmental cleanup of oil spills and biological treatment of refinery wastes (bioremediation).Other emerging applications include oil exploration, microbial enhanced oil recovery (MEOR), biodesulfurization and biodenitrogenation of distillates, biodemetallation, bioupgrading of heavy crudes and refining residues, valorization of refining wastes, bioconversion of residual oil to methane, control of oil field souring and corrosion, formulation of petrochemicals, etc. (
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".