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Record W3125043127

Ethical and Legal Issues in Assisted Reproductive Technology

2006· article· en· W3125043127 on OpenAlexaff
Bernard M. Dickens, Rebecca J. Cook

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEthical issuesReproductive technologyAssisted reproductive technologyBusinessSelection (genetic algorithm)Political scienceLawRisk analysis (engineering)Law and economicsPregnancyEngineering ethicsEngineeringEconomicsComputer scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

The potential and actual applications of reproductive technologies have been reviewed by many governmental committees, and laws have been enacted in several countries to accommodate, limit and regulate their use. Regulatory systems have nevertheless left some legal and ethical issues unresolved, and have caused other issues to arise. Issues that regulatory systems leave unresolved, or that systems have created, include disposal of embryos that remain after patients' treatments are concluded, and multiple implantation and pregnancy. This may result in risks to maternal, embryonic and neonatal life and health, and the contentious relief that may be achieved by selective reduction of multiple pregnancies. A further concern arises when clinics must (or choose to) publicize their success rates, and they compete for favorable statistics by questionable patient selection criteria and treatment priorities.

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)0.111
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.111
Threshold uncertainty score0.586

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.138
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0090.053
Scholarly communication0.0100.007
Open science0.0030.006
Research integrity0.0280.028
Insufficient payload (model declined to judge)0.0040.002

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 head0.010
GPT teacher head0.312
Teacher spread0.302 · 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 designNot applicable
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
Published2006
Admission routes1
Has abstractyes

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