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Record W2769904290 · doi:10.1503/cjs.016317

The 1917 Halifax Explosion: the first coordinated local civilian medical response to disaster in Canada

2017· article· en· W2769904290 on OpenAlexaffvenueabout
Chryssa McAlister, A. E. Marble, T. Jock Murray

Bibliographic record

VenueCanadian Journal of Surgery · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of TorontoDalhousie University
Fundersnot available
KeywordsMedicineTragedy (event)Mass-casualty incidentPreparednessMedical emergencyWork (physics)Mass CasualtyRelief WorkDisaster responseOccupational safety and healthMedical carePoison controlEmergency managementSuicide preventionDisaster planningLawFamily medicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

SUMMARY: The 1917 Halifax Explosion was an unfortunate but predictable tragedy, given the sea traffic and munitions cargo, resulting in sudden large-scale damage and catastrophic injuries, with 1950 dead and 8000 injured. Although generous support was received from the United States, the bulk of the medical work was undertaken using local resources through an immediate, massive, centrally coordinated medical response. The incredible care provided 100 years ago by these Canadian physicians, nurses and students is often forgotten, but deserves attention. The local medical response to the 1917 disaster is an early example of coordinated mass casualty relief, the first in Canada, and remains relevant to modern disaster preparedness planning. This commentary has an appendix, available at canjsurg.ca/016317-a1.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.690

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.005
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0090.001

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 head0.066
GPT teacher head0.336
Teacher spread0.270 · 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 designQualitative
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

Citations8
Published2017
Admission routes3
Has abstractyes

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