Progress Made by the South African Light Metals Development Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Through focused investment by the CSIR, the South African Innovation Fund, the Automotive Industry Development Centre and the Department of Science and Technology over the past eight years, the national Light Metals Development Network has been established and grown into a well aligned collaborative research and development programme. The research and development focus has been primarily on aluminium and titanium technologies as applied in the automotive and aerospace industry sectors, with the latter growing in prominence over the past three years. Since 2009 the titanium-related research and development activities have been consolidated in a Titanium Centre of Competence. This paper provides an overview of the current status of the programme, the R&D focus areas, the collaborating entities and the industry involvement. It also highlights some of the significant achievements of the network and notable outputs produced. The Titanium Centre of Competence as a vehicle for strengthening industrial research and innovation capabilities in specific fields of technology is discussed and some initial experiences are shared.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it