Bibliographic record
Abstract
“Hemorrhage, hemorrhage, hemorrhage—blood everywhere—clothes soaked in the blood, pools of blood in the stretchers, streams of blood dropping from the stretchers to the floor” Robertson OH, unpublished WWI diaries.1 In the sombre predawn darkness at 03:50 hours on the morning of 31July 1917; British, French and Belgian Forces advanced along the ridges and fields of the Gheluvelt plateau. So began the Third Battle of Ypres. Their objective was a small village called Passchendaele only 13 km away. However, it would take 3 months and over half a million casualties to get there. Two medical officers; both called Robertson, one Canadian and one American, were part of this offensive. The Canadian was Major Lawrence Bruce Robertson, a surgeon who used uncrossmatched whole blood transfused by syringe directly from donor to recipient to demonstrate the life-saving potential of blood transfusion and the need to resuscitate the badly injured with ‘something more than saline’ . He felt that any danger of this novel blood transfusion method was vastly outweighed by its benefit. He published his experiences of 36 such transfusions, performed in the 2nd Canadian Casualty Clearing Station in the BMJ of 1916.2 Unfortunately, three of his patients suffered fatal haemolytic reactions and died. Sir George Makins, the Surgeon General of the British Expeditionary Force (BEF) at the time, expressed considerable concern about this use of uncrossmatched blood transfusion within the BEF. An American officer, Captain Oswald Hope Robertson (US Army Medical Officer Reserve Corps), born in Woolwich, London, and emigrated with his parents to America when he was 18 months old, was sent to assess and solve the problem. From his past experience in the transfusion laboratories of Harvard Medical School and the Rockefeller Institute, OH …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.137 | 0.069 |
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".