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SYNTHETIC BIOLOGY AND GENETIC MANIPULATION: Risks, promises and responsibilities

2020· article· en· W3047871588 on OpenAlexaff
Roberto Rohregger, Anor Sganzerla, Daiane Priscila Simão-Silva

Bibliographic record

VenueAmbiente & sociedade · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsBioethicsEnvironmental ethicsCompromisePrudenceDignityEngineering ethicsTechnosciencePolitical scienceSynthetic biologySociologyBiologyEpistemologySocial scienceLawEngineeringPhilosophyGenetics

Abstract

fetched live from OpenAlex

Abstract As a result of the biotechnological advance, synthetic biology has been applied from the improvement of food to the creation of new organisms. This article investigates, from a bioethical perspective, the benefits, risks and threats to life, arising from the production, manipulation and, especially, the creation of synthesized DNAs that do not exist in nature. Bioethics reports from the White House and the Bioethics Committee of Portugal and Spain contributed to the discussion. The progress of technoscience, without the proper ethical capacity for evaluation, can produce results that compromise the social development, environmental preservation, human dignity, and biosphere life in the future. In this sense, the achievements of synthetic biology have been shown to be ambivalent, because hopes are mixed with threats, with unpredictable results to the diversity of life of the biosphere, which makes prudence the virtue par excellence.

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)0.026
metaresearch head score (Gemma)0.013
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.039
Scholarly communication0.0110.009
Open science0.0010.007
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.168
GPT teacher head0.365
Teacher spread0.197 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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