Inco banks $1bn as record earnings rocket 35 per cent
Bibliographic record
Abstract
Responsible mining companies have done much to redress the environmental damage of earlier technologies and continue to do more. In the Sudbury Basin, one of the most important mining areas in the world, both Inco Limited and Falconbridge Limited, two of the largest nickel producers, have significantly decreased sulphur dioxide emissions in the last 40 years from substantially 100% to about 10% or less of the sulphur in the ore; decreased water effluents by recycling; treated effluents to comply with government regulations; revegetated mine rock and surface tailings deposits and rehabilitated landscapes in the surrounding communities. Inco and Falconbridge continue to develop improved means for environmentally sound handling of all wastes including recycling and to reclaim land at abandoned mine sites. They have developed and implemented environmental policies and codes of practice, not only to comply with regulations, but to anticipate them. The mining industry recognises the need for regulation to protect human health and the environment. Existing regulations are based on a hazard assessment approach. A more realistic, pragmatic and cost-effective basis for regulation is risk management. This relates any documented effects to measured exposures and recognizes the need for exposure levels low enough that incidence of adverse health effects is as low as in the surrounding ecosystem.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.728 | 0.710 |
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".