Insights into deformation mechanisms from<i>in-situ</i>diffraction experiments
Bibliographic record
Abstract
All of the well-loved program systems have their roots in ideas formed about 30 years ago, and have evolved slowly under the care and attention of individuals or small groups.These programs express the knowledge held by these people, but they do not document it.The equation A T .A. x = A T .F sums up what happens in leastsquares, but it requires a lot of code to convert this into even a simple useable program, and a massive amount of understanding of the problem and environment to turn it into a user-friendly program.The principal writers and care-takers of the most popular programs are now in the final phases of their careers.When they shuffle off their mortal coils, devotees may be able to keep some of the programs running for a short while, as a kind of working museum.Every thing in not broken yet, so there is nothing to fix.However, if the community is to avoid re-inventing very may wheels in the future, there is urgent need to properly document current knowledge, and use it to create better wheels.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".