Production of Biomaterials from Solid Biomass Wastes for \nthe Mining Industry
Bibliographic record
Abstract
Newfoundland and Labrador has very rich mineral resources. The mining industry in this \nprovince produces more than a dozen different mineral commodities. On the other hand, \nthis province is producing huge amounts of solid biomass wastes everyday. Some of them \nare re-used, but mostly they are land-filled. With innovative, applied research these biomass \nwastes can be turned into more value added products for the mining industry. For example, \nwood wastes/saw dust, waste paper and waste cotton are some solid wastes that can easily \nbe converted into high-tech biomaterials (bioadsorbents) for gold recovery from a dilute \nsolution. Those solid wastes are cellulosic, they have a strong affinity to gold when \nconverted to its functional group in a very simple and cheap process. \nIn this project, we have carried out some innovative applied research to produce \nbioadsorbents derived from solid biomass wastes, such as waste paper, waste cotton and \nwaste wood/saw dust, which are abundantly available in this province and need proper \nmanagement to increase their life-cycle. Every day tons of waste paper and cardboard are \nbeing generated. Sources of waste cotton are old cloths, waste medical bandages, etc. \nWood waste is also very abundant in this province. When compared with the ever \nincreasing gold prices (currently ~$1700/oz), the face value of these solid wastes will jump \nfrom garbage to hi-tech market value. Proper management and effective use of such solid \nbiomass wastes as valuable bioadsorbents will not only reduce the volume of wastes being \ngenerated every day, but will also have a high end value to the gold mining industry as this \ncheap bioadsorbent will have superior performance over the traditionally used activated \ncarbon. Both the mining and waste management companies in this province will benefit from \nthis research.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".