Bibliographic record
Abstract
The mining operations conducted in Northern Ontario are generally considered to be among the richest deposits in the world. This extensive area includes multiple active mines, smelters, and refineries. A number of active waste dumps for tailings, slag, and waste rock also exist. It has been recognised that if current market conditions continue, and if the new reserve estimations are accurate, mining in this area could potentially continue for an additional 50 years. Operational difficulties for the organisations operating in this area arise from the fact that the mining operations are situated in some cases within the city limits and, in fact, also dominate a number of small communities around the mine sites. These organisations face a number of increasing regulatory and social demands which are a driving force behind many of the operational changes taking place within the mining community today. Rapidly, an environmentally conscious mining operation is becoming the norm. A solution to satisfying these escalating demands is the development and application of an integrated waste management strategy. Integrated waste management is a system in which the technical, environmental, social, economic, and risk aspects of the operation are studied. Following this a comparative analysis is conducted which balances the socio-economic benefits achieved with the organisation’s overall operating strategy. The integrated waste management strategy is ranked and scored using a ‘base case’ model. Multiple alternatives and suggestions are then made against established criteria in a number of categories. Sensitivity analyses are also established to determine the robustness of the trade-offs.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".