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Record W3011749667 · doi:10.1503/cjs.012318

Message in a bottle: the discovery of a young medical officer’s map from the 1917 Battle of Hill 70

2018· article· en· W3011749667 on OpenAlexaffvenueabout
Michael T. Kryshtalskyj, Jonathan F. Vance, Chryssa McAlister

Bibliographic record

VenueCanadian Journal of Surgery · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsBattleMedicineOfficerFront (military)Spanish Civil WarAncient historyHistoryArchaeologyEngineering

Abstract

fetched live from OpenAlex

Summary: We report the serendipitous discovery of a map drawn by an army surgeon during the First World War. The map, entitled “Loos 36° NW3,’”was drawn by 24-year-old Captain Alexander Edward MacDonald in fall 1917 and was found in his old surgery textbook. MacDonald’s map depicts the positions of Canadian frontlines and medical units after the Battle of Hill 70. During the battle, Dr. MacDonald tended to the wounded in an aid post that he constructed in a ruined coal mine near the Front. MacDonald would go on to serve with distinction in the Battle of Passchendaele and Canada’s Hundred Days, and he received the Military Cross for gallantry. He maintained a passionate interest in cartography throughout his life and eventually became an authority among map collectors. Artifacts such as MacDonald’s map remind us of the realities of war and the sacrifices of our surgeon predecessors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.971
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.007
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0080.003

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 head0.030
GPT teacher head0.207
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes3
Has abstractyes

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