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Record W2149672766 · doi:10.4269/ajtmh.2008.79.312

Ethical, Social, and Cultural Considerations for Site Selection for Research with Genetically Modified Mosquitoes

2008· article· en· W2149672766 on OpenAlexaff
James V. Lavery, Laura C. Harrington, Thomas W. Scott

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of TorontoCentre for Global Health ResearchSt. Michael's Hospital
Fundersnot available
KeywordsSelection (genetic algorithm)Process (computing)Site selectionGenetically modified organismKey (lock)BiotechnologyBiologyManagement scienceEngineering ethicsRisk analysis (engineering)Computer sciencePolitical scienceEngineeringBusinessEcologyArtificial intelligenceGenetics

Abstract

fetched live from OpenAlex

Recent advances in technology have made strategies for disease control using genetically modified (GM) vectors more plausible. Selecting an appropriate field site for research with GM mosquitoes may be one of the most complex and significant aspects of the research process. Among the key considerations of the process is the need to address ethical, legal, and cultural (ESC) issues. No guidelines have been developed to date for this complicated and sensitive process. In this paper, we describe a site selection process and a set of preliminary considerations for addressing the ESC aspects of a research program involving genetic strategies for the control of mosquitoes as vectors for dengue viruses. These considerations reflect some of the key ESC issues for site selection decisions for research with GM vectors.

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)0.106
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.106
Threshold uncertainty score0.559

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1060.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0030.002

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 head0.073
GPT teacher head0.407
Teacher spread0.334 · 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 designTheoretical or conceptual
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

Citations78
Published2008
Admission routes1
Has abstractyes

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