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Record W2163498625 · doi:10.5779/hypothesis.v11i1.330

Nanotechnology risks: A 10-step risk management model in nanotechnology projects

2013· article· en· W2163498625 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHypothesis · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsnot available
Fundersnot available
KeywordsNanotechnologyRisk managementSocietal impact of nanotechnologyImpact of nanotechnologyEngineeringRisk analysis (engineering)Materials scienceBusiness

Abstract

fetched live from OpenAlex

The use and handling of par- ticulate material between 1 and 100 nm, also known as nanotechnology, has been shown to have potential to revolution- ize many aspects of industries, medical practices and the human environment. However, very little is known about the risks and hazards of nanomaterials to humans and the environment, so a con- servative approach is encouraged. This article proposes a 10- step qualitative risk management model for nanotechnology project managers, which enables them to detect significant risks in a systematic approach and provide decisions and suit- able actions regarding the health of their employees. INTRODUCTION The properties of mate- rials at atomic and molecular size range (between 1 and 100 nm) are significantly different from those at a larger scale. This emerging technology is based on the size and surface condition of solid par- ticles 1,2

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.041
GPT teacher head0.248
Teacher spread0.207 · 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