Will Kill a Guinea Pig Club supporter
Bibliographic record
Abstract
This is about William Henry Kill, my father. He originally joined the RAF in 1926, then came out of the service, and re-joined when war broke out in 1939. He was in the Royal Air Force, stationed at Upper Heyford, Lyneham, Bicester, and later on in Canada.Whilst he was stationed at Upper Heyford, a plane crashed near our house, and my father and another man rescued the pilot from the burning aircraft. The pilot was very badly burned, but was taken to hospital, and did make a recovery. He subsequently returned to South Africa, but kept in touch with my father, and sent food parcels from South Africa to our family.My father became a lifelong supporter of the Guinea Pig Club which was formed in July 1941 to support aircrew who were undergoing reconstructive plastic surgery after receiving burn injuries in the Second World War .My father was later sent to Canada with the RAF, and went to Vancouver, and then to Banff. He was a carpenter by trade and whilst at Banff he was asked by the chaplain to make a font for the Chapel. I do not know what work he was actually doing whilst at Banff. We have postcards which he sent from Vancouver and British Columbia, but nothing from his time at Banff.The photograph of my father wearing First Nations head dress was taken in Banff when he was stationed there.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.331 | 0.168 |
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".