MétaCan
Menu
Back to cohort
Record W1510549050 · doi:10.1520/jfs14642j

Ground Penetrating Radar Surveys to Locate 1918 Spanish Flu Victims in Permafrost

2000· article· en· W1510549050 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Forensic Sciences · 2000
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of WindsorGeological Survey of CanadaHospital for Sick ChildrenResearch Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Cancer Institute
KeywordsPermafrostGround-penetrating radarPoison controlRadarGeologyRemote sensingForensic engineeringEngineeringMedical emergencyMedicineOceanographyAerospace engineering

Abstract

fetched live from OpenAlex

The "Spanish Flu" killed over 40 million people worldwide in 1918. Archival records helped us identify seven men who died of influenza in 1918 and were interred in Longyearbyen, Svalbard, Norway, 1,300 km from the North Pole. Ground Penetrating Radar (GPR) was used successfully, in a high-resolution field survey mode, to locate a large excavation with seven coffins, near the existing seven grave markers. The GPR indicated that the ground was disturbed to 2 m depth and was frozen below 1 m. Subsequent excavation showed that: a) the GPR located the position of the graves accurately, b) the coffins were buried less than 1 m deep, and c) that the frozen ground was 1.2 m deep where the coffins were located. The GPR assisted in planning the exhumation, safely and economically, under the high degree of containment required. Virologic and bacteriologic investigations on recovered tissues may give us an opportunity to isolate and identify the micro-organisms involved in the 1918 influenza and expand our knowledge on the pathogenesis of influenza.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.278
Teacher spread0.248 · 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