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Record W4290374220

Foreign medical missions in Serbia in the First World War

2019· article· en· W4290374220 on OpenAlexaboutno aff
Lopičić-Jančić Jelena, Vasić Ljubica

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBalkan and Eastern European Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyHistoryAncient historyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

During the First World War, from 1914 until 1915, Serbia has been subjected to aggression and occupation by the Austro-Hungarian Empire. During the entire period of aggression and occupation, the Austro-Hungarian army and the local authorities carried out severe war crimes against wounded, sick, medical personnel and the civilian population. In addition to the aforementioned in the year of 1915, a major epidemic of all three types of typhoid occurred in Serbia. Because of the war and epidemic of typhoid the medical situation in Serbia was very difficult so a large number of medical missions from Allied and neutral countries have come to Serbia to help. The medical missions came from Russia, Great Britain, Canada, Greece Australia and New Zealand, as well as from neutral countries such as Switzerland, the USA, Netherlands and Denmark. These medical missions have managed to provide great assistance in rescuing wounded and sick in Serbia. Also, a large number of doctors and medical personnel from these missions have died or suffered from infectious diseases.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.257
GPT teacher head0.462
Teacher spread0.205 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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