Bibliographic record
Abstract
A plan of action is proposed to develop uses of non-ferrous slag and production of certain industrial materials from this waste material. A review of chemistry of non-ferrous slags suggested that it can be reprocessed to impart "cementitious" properties and furthermore an advantage could be taken of their latent "pozzolonaic" properties to test several potential applications such as - clinker ingredient, asphalt concrete additive, cemented mine backfill and binder for base stabilization. The authors note that in the last century blast furnace slag was considered as a "waste" product whose stocks grew at an alarming rate due to decades of accumulation and iron producers had to develop uses of this material to avoid a potentially catastrophic environmental situation. Non-ferrous slag producers could emulate this example set by the iron producers who successfully converted a "waste" into a "byproduct". Environmental concerns of the new millennium demand that the industry should find ways and means of depleting the ever-growing stockpiles of non-ferrous slag. The authors conclude that serious considerations from environmental and economic fronts favor reclamation of old slag dumps as well as processing of new non-ferrous slag as a viable alternative to the existing dumping practice.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".