Investigations Continue into Mysterious Illness Affecting U.S. Officials in Havana and Elsewhere
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".