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Record W1998974421 · doi:10.1177/0270467604263120

Microsystems and Nanoscience for Biomedical Applications: A View to the Future

2004· article· en· W1998974421 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBulletin of Science Technology & Society · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of CalgaryUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsMicrosystemEmerging technologiesEngineering ethicsQuality (philosophy)Ethical issuesEngineeringNanotechnologyManagement scienceComputer scienceRisk analysis (engineering)BusinessArtificial intelligence

Abstract

fetched live from OpenAlex

At present there is an enormous discrepancy between our nanotechnological capabilities (particularly our nanobiotechnologies), our social wisdom, and consensus on how to apply them. To date, cost considerations have greatly constrained our application of nanotechnologies. However, novel advances in microsystem platform technologies are about to greatly diminish that economic constraint while developing new industries. Properly used in a solid legal and ethical framework, within an educated population, these advances will vastly enrich our quality of life without being intrusive. Improperly used, these technologies could lead to a modern-day Luddism, social turmoil, or possibly even to emulating those societies described in the darkest of novels. These technologies must be developed in tandem with the social and legal frameworks needed to ensure that they improve both individuals and our society. To ensure that this occurs, we need to have the ethical, legal, scientific, and engineering experts working together and with the public.

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.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.003
GPT teacher head0.272
Teacher spread0.268 · 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