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Record W2792316378 · doi:10.2218/resmedica.v24i1.2508

Henry Gray and John Fraser: Scottish surgeons of the Great War

2017· article· en· W2792316378 on OpenAlexaboutno aff
Tom Scotland

Bibliographic record

VenueRes Medica · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsBattleQuarter (Canadian coin)Gray (unit)MedicineHistoryMedical servicesAncient historyFirst world warDemographyLawPolitical scienceArchaeologySociology

Abstract

fetched live from OpenAlex

Between 1914 and 1918, the British Expeditionary Force fighting in France and Flanders sustained 2.7 million battle casualties. Just over one quarter (26.1%) were never seen by the medical services. These were men who had been killed (14.2%), were missing (5.4%), or were prisoners of war (6.5%). Most of those who were missing had been killed and their bodies never recovered. Just under three-quarters of the wounded (73.9% or 1 988 969) were seen and treated by the medical services and 151 356 died.[i] The worst single day in British military history was Saturday 1 July 1916, the first day of the Battle of the Somme, when there were 57 470 casualties, of whom 20 000 were killed or died from their wounds. In nearly a quarter of a million admissions dealt with by the medical services, 58.5% of wounds were caused by high-explosive shellfire, 39% by bullets (mostly from machine guns), 2% were caused by grenades, and 0.5% from bayonets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.533
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.031
GPT teacher head0.241
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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