Bibliographic record
Abstract
Abstract John Herbert Beynon was born in a Welsh mining village in 1923, graduated BSc (Hons, physics) at 19, then worked on weapons research for the war effort in World War Two. In 1947 he started a 22-year career at Imperial Chemical Industries (ICI) in Manchester, where, with his technician Albert Williams, he revolutionized mass spectrometry from a physics method to an essential technique for organic chemistry. In 1960 he completed his landmark book Mass spectrometry and its applications to organic chemistry, which was reprinted by the American Society for Mass Spectrometry as a ‘Classic Book’. He continued his creative work in 1969–1974 at Purdue University. In 1974 he started his Royal Society Research Professorship at Swansea University, his alma mater. Collaborations here with a long string of students, postdoc, visiting professors and scientists from many countries resulted in about 65% of his list of 395 peer-reviewed publications. His unexpected retirement led to a compendium of 32 articles in his honour. He then initiated and edited a new journal, Rapid Communications in Mass Spectrometry.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.196 | 0.118 |
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".