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Ecological Risk Analysis and Genetically Modified Salmon: Management in the Face of Uncertainty

2014· review· en· W2120986638 on OpenAlexaff
Darek T. R. Moreau

Bibliographic record

VenueAnnual Review of Animal Biosciences · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsGovernment of Newfoundland and Labrador
Fundersnot available
KeywordsCommercializationGenetically modified organismRisk managementBiologyFace (sociological concept)Process (computing)Risk analysis (engineering)EcologyEnvironmental resource managementBusinessSociologyEconomicsComputer science

Abstract

fetched live from OpenAlex

The commercialization of growth hormone transgenic Atlantic salmon for aquaculture has become a controversial public policy issue. Concerns exist over the potential ecological effects of this biotechnology should animals escape captivity. From within an ecological risk-analysis framework, science has been sought to provide decision makers with evidence upon which to base regulatory decisions pertaining to genetically modified salmon. Here I review the available empirical information on the potential ecological and genetic effects of transgenic salmon and discuss the underlying eco-evolutionary science behind the topic. I conclude that data gaps and irreducible epistemic uncertainties limit the role of scientific inference in support of ecological risk management for transgenic salmon. I argue that predictive uncertainties are pervasive in complex eco-evolutionary systems and that it behooves those involved in the risk-analysis process to accept and communicate these limitations in the interest of timely, clear, and cautious risk-management options.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.332
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2014
Admission routes1
Has abstractyes

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