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Record W3066853100 · doi:10.1071/ma20031

Biological warfare: the history of microbial pathogens, biotoxins and emerging threats

2020· article· en· W3066853100 on OpenAlex
Alexa Kaufer, Torsten Theis, Katherine A. Lau, Joanna L Gray, William D. Rawlinson

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

VenueMicrobiology Australia · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsBiological warfareTerrorismPublicityCivilian populationOrganismPopulationBiodefenseComputer securityMedicineEnvironmental healthBiologyPolitical scienceComputer scienceLawToxicologyMicrobiology

Abstract

fetched live from OpenAlex

Bioterrorism is the deliberate misuse of a pathogen (virus, bacterium or other disease-causing microorganisms) or biotoxin (poisonous substance produced by an organism) to cause illness and death amongst the population. Bioterrorism and biological warfare (biowarfare) are terms often used interchangeably. However, bioterrorism is typically attributed to the politically motivated use of biological weapons by a rogue state, terrorist organisation or rogue individual whereas biological warfare refers to a country’s use of bioweapons. Although rare, bioterrorism is a rapidly evolving threat to global security due to significant advancements in biotechnology in recent years and the severity of agents that could be exploited. The pursuit of publicity plays a vital role in bioterrorism. The success of a biological attack is often calculated by the extent of terror resulting from the event, psychological disruption of society and political breakdown, rather than the lethal effects of the agent used.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.459

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 studies0.0000.001
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 head0.065
GPT teacher head0.281
Teacher spread0.216 · 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