Rusty's War: Courage in the Clouds
Bibliographic record
Abstract
Russell, or as he is more commonly called Rusty, flew Lancaster Bombers on numerous raids over Europe including the 1000 bomber raids.Firstly, he did not have any fear or superstitions as he is sure he had his father’s good luck. He joined the RAF at seventeen and his father was in the Navy. He became a pilot flying Lancaster Bombers.On some of the raids, there were higher losses amongst the British Bombers than on others, depending on the ground defences as well as the night fighters. On one raid of 240 aircrafts, they lost 83 over enemy territory after flying a long straight leg of the raid. On another, only 26 aircrafts were sent and they lost 7 aircrafts.There were also mid-air collisions between our aircrafts as we were flying close formations.The one time we had the bomb aimers controls cut through by the props of another aircraft below but we carried on. On another occasion, all the hydraulics to the Pilots controls were cut and so he asked the crew if they should continue or turn back and they all asked to continue, which is what they did and eventually dropped their bomb load and managed to turn the aircraft round and slowly flew back to the UK fearful of being "picked" off. They managed to get the aircraft back over the English coast and crash landed the aircraft BUT saved the crew!After reaching 30 operational missions, he went to Canada to train for a special duty squadron.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".