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Record W4236921699 · doi:10.1017/s0033291702009741

Highlights in this issue

2002· article· en· W4236921699 on OpenAlexaboutno aff

Bibliographic record

VenuePsychological Medicine · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSomatizationGulf warDepression (economics)MoodPsychiatrySubject (documents)PsychologyMood disordersPolitical scienceHistoryLibrary scienceMental healthEconomic historyAnxiety

Abstract

fetched live from OpenAlex

This issue features groups of papers on Gulf War syndrome, health service outcome assessment, somatization, personality disorders and depression. Gulf War syndrome has been the subject of much scientific research and public controversy. Two research papers, both from the group at the Institute of Psychiatry in London report studies. David et al. (pp. 1357–1370) find cognitive abnormalities in Gulf War veterans compared with military controls, most attributable to mood disturbances, except for impairment of constructional ability. Everitt et al. (pp. 1371–1378) employ the statistical technique of cluster analysis to search for a unique syndrome in Gulf War veterans but fail to find it. In an accompanying editorial two authorities, from the USA and Canada, examine the issues.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.113
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0030.002
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.1130.059

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 head0.050
GPT teacher head0.324
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2002
Admission routes1
Has abstractyes

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