Bibliographic record
Abstract
Abstract Some definitions and criteria for classification of refractories as engineering materials are given. How they are made, and information is given on how production of refractories results from the culmination of many different technologies, and that they are tailored to meet very specific needs and construction. The raw materials to make refractories: oxides, carbons, other non‐oxides, other chemicals, including cements and fibers (organic, metallic, ceramic) are detailed, distinguishing for each category between the ores, the minerals and the raw material Information on the characteristics of refractories as engineered materials include: properties versus the characteristics, the scales (magnification) and the hierarchy of structures; testing methods and the physical, mechanical and chemical characteristics of the refractories (and raw materials); properties of pure oxides and other synthetic products, relevant for the refractories products. Refractory lining design, selection of products, construction, installation, and maintenance are major points covered. The materials and the construction of lining (including anchoring), state of stresses in linings, the gradient of temperature in use, and all other process variables, 1‐2‐3 walls, insulating, heat transfer calculations, listing of properties for selected materials are given. Detailing the use of refractories in various industrial applications fall outside the scope of this article, but several sources of references have been provided.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.026 |
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".