Production of Biomaterials from Solid Biomass Wastes forthe Mining Industry
Bibliographic record
Abstract
Newfoundland and Labrador has very rich mineral resources. The mining industry in this province produces more than a dozen different mineral commodities. On the other hand, this province is producing huge amounts of solid biomass wastes everyday. Some of them are re-used, but mostly they are land-filled. With innovative, applied research these biomass wastes can be turned into more value added products for the mining industry. For example, wood wastes/saw dust, waste paper and waste cotton are some solid wastes that can easily be converted into high-tech biomaterials (bioadsorbents) for gold recovery from a dilute solution. Those solid wastes are cellulosic, they have a strong affinity to gold when converted to its functional group in a very simple and cheap process. In this project, we have carried out some innovative applied research to produce bioadsorbents derived from solid biomass wastes, such as waste paper, waste cotton and waste wood/saw dust, which are abundantly available in this province and need proper management to increase their life-cycle. Every day tons of waste paper and cardboard are being generated. Sources of waste cotton are old cloths, waste medical bandages, etc. Wood waste is also very abundant in this province. When compared with the ever increasing gold prices (currently ~$1700/oz), the face value of these solid wastes will jump from garbage to hi-tech market value. Proper management and effective use of such solid biomass wastes as valuable bioadsorbents will not only reduce the volume of wastes being generated every day, but will also have a high end value to the gold mining industry as this cheap bioadsorbent will have superior performance over the traditionally used activated carbon. Both the mining and waste management companies in this province will benefit from this research.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".