Bibliographic record
Abstract
The continued depletion of high-grade ores and growing awareness of environmental degradation associated with the traditional methods have provided impetus to explore simple, efcient and less polluting biological methods in uranium mining, processing, and waste water treatments (Torma, 1983; Bosecker, 1990). Hydrometallurgical methods have some disadvantages such as poor recovery, involvement of high process and energy cost, and increases in the pollution load of water resources (Bruynesteyn, 1989; Dwivedy and Mathur, 1995). Uranium could also be recovered by microorganisms that catalyse the oxidation and reduction of uranium and associated metals also, and hence inuence their mobility in the environment. Industrial-scale bioleaching of uranium is carried out by spraying stope walls with acid mine drainage and the in situ irrigation of fractured underground ore deposits. The recent upsurge of interest in this area is motivated by the fact that it is simple, effective, and potentially a relatively lessexpensive process involving low-energy that is environmentally benign; this is due to the uranium solubilising and accumulating properties of certain microorganisms. Besides, its industrial application to ensure the supply of raw material for producing energy, microbial leaching has a denite potential for remediation of mining sites, treatment of wastes and detoxication of sewage sludge (Brierley and Brierley, 1999). The commercial application of bioleaching of uranium from low-grade ores has been practiced since the 1960s. The seven leading uranium-producing countries in descending order are Canada, Australia, Niger, the Russian Federation, Kazakhstan, Namibia and Uzbekistan. Currently, the two largest producers, namely, Canada and Australia alone account for over 50% of global uranium production. In regard to bioleaching, Canada produced about 70,000 lb of U3O8 in 1977 at Agnew Lake Mine, Ontario, Canada from its ore using Acidithiobacillus ferrooxidans (A. ferrooxidans) (McCready and Gould, 1990). Commercial scale experience has been limited to the operations at Denison’s Elliot Lake, Ontario, Canada in the 1980s and the dump bioleaching at the Gibraltar Mine, British Columbia. The presence of microorganisms in leaching operations has been found to be benecial in catalysing the uranium dissolution process (Brierley, 1997; Brierley and Brierley, 1999). Currently, this technology is applied on a commercial scale not only for the recovery of uranium but also for extraction of copper, nickel, gold and so forth through heap, dump and in situ leach techniques (Torma, 1983; Torma and Banhegyi, 1984; Mwaba, 1991; Elshafeea et al., 2014). The process ow sheet for the bioleaching of uranium is shown in Figure 3.1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".