Industrial waste minimisation in South Africa : a case study in the textile and metal finishing sectors.
Bibliographic record
Abstract
ABSTRACT ii Environmental legislation is becoming more stringent as people are realising the need for conservation and a reduction of environmental degradation in order to facilitate sustainable development. To ease legislative pressures, companies need to work together in symbiotic networks, whereby co-operation between companies results in far more innovative practices than if the companies acted individually. Success in an industrial network is largely dependent on cleaner production, where industries seek to redirect from waste treatment to waste minimisation. Cleaner production has already received international recognition and waste minimisation initiatives have been used as a tool of cleaner production. Two polluting industrial sectors within South Africa, the textile and metal finishing sectors, were chosen to investigate waste minimisation concepts. One company from each sector was used as a case study. The dissertation followed company network identification, potential to participate within an industrial symbiotic network, and waste minimisation opportunities. Suppliers and buyers, up and down the product line were identified. Relationships with these partners should be advanced such that environmental concerns are at the
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".