Bibliographic record
Abstract
Ainsley Iggo's research was focused on the functional properties of sensory receptors in the skin and viscera. He developed a new electrophysiological technique for recording the electrical activity of individual afferent fibres and was the first to record such activity from single unmyelinated afferents, the smallest diameter afferents in sensory nerves. His seminal work contributed to the discovery of nociceptors; the sensory receptors that respond to injury and are at the origin of pain sensation. He also recorded the functional activity of many types of sensory receptor in the skin, muscle and viscera and classified their responses according to their adequate stimuli. These findings gave support to the specificity theory of sensation, particularly of pain. He described the morphology of individually identified receptors, thus providing direct evidence for the long-held assumption that distinct morphological types of skin receptors mediate distinct sensations. Later in life he contributed to studies of sensory neurons in the spinal cord and of the sensory electro-receptors found in animals such as the echidna and the platypus. A native of New Zealand, he moved to the UK in 1950 and spent most of his professional life at the University of Edinburgh, where he created a highly productive research group at the Veterinary School.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.268 | 0.127 |
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".