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Record W3112210269 · doi:10.3329/bioethics.v10i2.50660

Societal Concerns with Biotechnology and Necessity of Regulations

2019· article· en· W3112210269 on OpenAlexaff
Abu Sadat Mohammad Nurunnabi, Miliva Mozaffor, Mariya Tabassum, Taohidur Rahman Saikat, Nahid Kabir, Mohammad Akram Hossain

Bibliographic record

VenueBangladesh Journal of Bioethics · 2019
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsBridgepoint Active Healthcare
Fundersnot available
KeywordsBiotechnologyAgricultureBusinessEthical issuesEngineering ethicsEngineeringRisk analysis (engineering)Biology

Abstract

fetched live from OpenAlex

Biotechnology is the use of living systems and organisms to develop or make products, or any technological application that uses biological systems, living organisms or derivatives to make or modify products or processes for specific use. Biotechnology is a constantly evolving field of modern science. New tools and products developed by biotechnologists are useful in research, agriculture, industry and healthcare. Although it has many benefits including lowering our environmental footprint, and helping in diagnosis and treatment of diseases, it comes with its all-possible disadvantages. The four main societal concerns revolve around are ethical, safety, bioterrorism and environmental issues. This paper aims to describe those societal concerns raised by applications of biotechnology and possible regulations related to biotech innovations and policy implementation.

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)0.023
metaresearch head score (Gemma)0.038
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 score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.018
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0160.013
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 head0.026
GPT teacher head0.301
Teacher spread0.276 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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