Bibliographic record
Abstract
An experimental analysis was carried out by treatment of used hydraulic oil with various concentrations of fullers' earth and the results obtained were compared with fresh oil. Properties tested for include viscosity, specific gravity, absorbance and wear metals. The results obtained showed an improvement in the depleted quality of oil; in particular the viscosity falls within ± 15% tolerance of the fresh oil value as recommended by ASTM standards. It is recommended that the reclaimed oil obtained should be re-blended with additive or fresh oil in a pre-determined ratio using ASTM blending chart for further enhancement of additive content. It is concluded that reclamation of used oil is an environmentally friendly way of dispersing waste oils which can serve as a waste to wealth method. The quality of the environment has been adversely affected by the activities of the petroleum industry right from the exploration, drilling, transportation and processing to storage and after use. Hence, the industry discharges waste which comes in gaseous, liquid or solid form into the environment; thus threatening the health of the population and in the long run affecting farmland and water bodies. In Nigeria, an average of about 300 million liters of lubricating oils is consumed annually with a market potential of 5% annual growth rate. The potential waste oil level is about 80% of the annual consumption and thus the environment is prone to about 240 million liters of used lubricating which requires management and control. It is also observed that the awareness for waste oil management and its investment potentials are not well accepted and inculcated; rather it is seen as an anti-economic issue and a necessary debt to be paid for industrialization of urban centers. In some cases, it is due to lack of adequate information on cost effective methods of oil management, for example in Canada approximately about 1billion liters of lubricating oils are sold annually but only about 200million liters are recovered, in other words more than half is wasted and disposed to the environment (2) . In Nigeria, despite the increase in the presence of Lube oil blending plants ( both foreign and local companies), It is quite disappointing to know that the level of recycling oil is low and thus cannot meet up with the requirement of waste oil management as stipulated by the Federal Environmental Protection Agency (FEPA) that: No oil in any form shall be discharged into public drains, rivers, lakes, seas or underground injection without permit issued by the agency or any organization so designated by FEPA (2) .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".