Aluminium alloys 2006 : research through innovation and technology : proceedings of the 10th International Conference on Aluminium Alloys, Vancouver, Canada, July 9th-13th, 2006
Bibliographic record
Abstract
The world production of primary and recycled aluminum continues to increase and, over the past twenty years, has risen from '15 Mt/y in 1985 to '32 Mt/y in 2005. The main consumers are transportation, beverage and other packaging, and building construction. The global primary aluminum production has been growing by about 2-3 per cent per year. However, growth rates over the last decade have been much higher. In particular, during the past five years, China has played a critical role in aluminum production and has gone through a dramatic period of growth. The specific topics considered include: alloys and phase transformations, corrosion and surface modification, deformation and formability, fatigue, fracture and creep, joining technologies, new directions, novel experimental techniques, processing and process modelling, recovery, recrystallization and texture, solidification and casting. Overall, this collection of papers represents a seminal history of the state of knowledge in the aluminum industry, related to the processing and properties of aluminum alloys and, as such, will further contribute to this basic field of knowledge.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.037 |
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".