Benedict Delisle Burns - Publications from Benedict Delisle Burns. 22 February 1915—6 September 2001
Bibliographic record
Abstract
Ben Burns was a pioneer of operations research and of the statistical analysis of neuronal activity. During the war, Ben served in Solly Zuckermann's operations research unit, which included a period of active service in the Mediterranean. After the war he worked with G. L. Brown at the National Institute for Medical Research (NIMR), where he investigated the effects of agents that affected neuromuscular transmission. In 1950 he moved to the Physiology Department of McGill University in Montreal, where he explored the properties of neural networks in neurologically isolated slabs of cerebral cortex and established the mechanisms responsible for maintaining rhythmic periods of excitation in isolated nerve networks. He subsequently provided evidence that self-re-exciting neural networks were implicated in establishing the respiratory rhythm. While at McGill, Ben initiated a number of highly original cross-disciplinary studies concerning the physiological bases of learning, memory and attention. He returned to NIMR in 1966 to head the Division of Physiology and Pharmacology, where he continued his investigations of visual perception. Ben was an ingenious experimenter and devised a number of mechanical and electronic devices for the statistical analysis of nerve cell activity at a time when digital computers were largely unavailable for biological work. In his 1968 book, The uncertain nervous system, he expressed his view that the interdisciplinary nature of central neurophysiology required of those who studied it a knowledge of classical physiology, experimental psychology, applied mathematics and electronic engineering. His broad view of the subject inspired a generation of students.
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 | 0.002 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.340 | 0.174 |
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".