Supplementary material from "Diamond Thin Films: A 21st Century Material. Part 2: A New Hope"
Bibliographic record
Abstract
Nearly a quarter of a century ago, we wrote a review paper about the very new technology of chemical vapour deposition of diamond thin films. We now bring the story up to date by describing the progress made – or not made – over the intervening years. Back in the 1990s and early 2000s there was enormous excitement about the plethora of applications that were suddenly possible now that diamond could be fabricated in the form of thin films. Diamond was hailed as the ultimate semiconductor, and it was believed that the few remaining problems would quickly be solved leading to a new ‘diamond age’ of electronics. In reality, however, difficulty in making large-area diamond wafers, and the elusiveness of a useful n-type dopant, slowed progress substantially. Unsurprisingly, over the following decade, the enthusiasm and funding for diamond faded, while competing materials forged ahead. But in early 2010’s, several new game-changing applications for diamond were discovered, such as electrochemical electrodes, the NV-centre defect which promised room-temperature quantum computers, and methods to grow large single-crystal gemstone-quality diamond. These led to a resurgence in diamond research and a new hope that diamond might finally live up to its promise.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.745 | 0.396 |
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".