Bibliographic record
Abstract
The ECCC (European Creep Collaborative Committee) approach to creep data assessment has now been established for almost 10 years. The methodology covers the analysis of rupture strength and ductility, creep strain and stress relaxation data, for a range of material conditions. This paper reviews the concepts and procedures involved. The original approach was devised to determine Data Sheets for use by committees responsible for the preparation of National and International Design and Product Standards, and the methods developed for data quality evaluation and data analysis were therefore intentionally rigorous. The focus was clearly on the determination of long time property values from the largest possible datasets involving a significant number of observations in the mechanism regime for which predictions were required. More recently, the emphasis has changed. There is now an increasing requirement for full property descriptions from very short times to very long, and hence the need for much more flexible model representations than were previously required. There continues to be a requirement for reliable long time predictions from relatively small datasets comprising relatively short duration tests, in particular to exploit new alloy developments at the earliest practical opportunity. In such circumstances, it is not feasible to apply the same degree of rigour adopted for large dataset assessment. Current developments are reviewed.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".