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Record W3189088911 · doi:10.1017/ajil.2021.27

Investigations Continue into Mysterious Illness Affecting U.S. Officials in Havana and Elsewhere

2021· article· en· W3189088911 on OpenAlexaboutno aff
Jack Hoover, Kevin Krotz, Pierce Macconaghy, Kyle Mcgoey, Margaret Shin

Bibliographic record

VenueAmerican Journal of International Law · 2021
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Administration (probate law)ChinaHeadachesMedicinePolitical scienceFamily medicinePsychiatryLaw

Abstract

fetched live from OpenAlex

In late 2016, officials at U.S. and Canadian diplomatic posts in Havana, Cuba, began reporting a cluster of symptoms, including nausea, severe headaches, and dizziness, that came to be known as “Havana Syndrome.” The illnesses prompted the departure of U.S. personnel from Havana in 2017, and subsequent cases have been reported among personnel assigned to the U.S. consulate in Guangzhou, China and elsewhere. In recent months, three reports related to the illnesses and the government's response have become public. A Center for Disease Control (CDC) report examined medical records and produced a case definition. A National Academies for Sciences, Engineering, and Medicine (NASEM) standing committee considered possible causes, including “directed, pulsed radio frequency (RF) energy, . . . chemical exposures, infectious diseases and psychological issues,” and concluded that the symptoms were consistent with RF effects. Additionally, a recently declassified Accountability Review Board (ARB) report criticized the Trump administration's response to the illnesses in Havana. Amid reports of increasing cases, including some occurring in the United States, the Biden administration is attempting to determine the cause of the illness and has committed to support affected personnel.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.284

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 studies0.0000.000
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 head0.012
GPT teacher head0.300
Teacher spread0.288 · 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.

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
Published2021
Admission routes1
Has abstractyes

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