Bibliographic record
Abstract
Through identification of by-products available and utilizing existing and emerging technologies, opportunities to manage and use waste to solve problems can be and have been developed. By examining synergies between by-products across different industries and within mining to reuse material by-products, the waste can be diverted from landfill or other long-term storage facilities, thereby reducing the industry environmental impacts. The industries specifically discussed and identified in the case studies are forestry, water treatment, mining, and agriculture, however the focus is on the mining industry. Each industry examined has byproducts that have favourable material properties that solve problems or reduce issues. By evaluating alternate waste products that can be used in construction or co-disposed with, industry professionals can reduce the environmental impact and effective cost of waste management for the industries involved. Rebranding waste as a material by-product can change the perception that waste is only a burden; thereby turning waste into a viable material resource. While technology and innovation are available for responsible and environmentally aware material use, practices within the mining industry have remained the same. The case studies included outline some use of material by-products to demonstrate and support the concept of waste reuse and ultimately waste as a resource. This paper encourages awareness of the inconspicuous details of seemingly unconnected industries that are often overlooked. The mining industry and all industries need to respect natural resources, to use as much of the available materials and waste as little as possible. With more public awareness around sustainability, the environment, impacts of industry on the environment and climate change, regulations are likely to start including social license into legislated requirements to operate. Industry practices are being closely examined in more detail, with a focus not only on the companies acting ethically but also on the individual practitioners. To be socially acceptable and environmentally sustainable the mining industry and industry professionals must practice better waste management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".