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Record W1997289098 · doi:10.1177/1091581810370720

A Method to Assess the Quality of Studies That Examine the Toxicity of Engineered Nanomaterials

2010· article· en· W1997289098 on OpenAlexaff
Jeffrey W. Card, Bernadene A. Magnuson

Bibliographic record

VenueInternational Journal of Toxicology · 2010
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsCantox Health Sciences InternationalHealth Sciences North
Fundersnot available
KeywordsNanomaterialsReliability (semiconductor)Quality (philosophy)Computer scienceRisk analysis (engineering)NanotechnologyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

As reports on the safety of various nanomaterials have yielded conflicting results, assessment of the reliability of each study is required to objectively interpret overall safety of the nanomaterial. A 2-step method to assess the quality of nanotoxicity studies is described. The first step uses a publicly available tool to rank the reliability of the study based on adequacy of design and documentation of methods, materials, and results, providing a "study score." The second step determines the completeness of physicochemical characterization of the nanomaterial/nanomaterials assessed within the study, providing a "nanomaterial score." This approach is encouraged to promote the notion that for studies conducted with nanomaterials, the combination of a reliable study and sufficient nanomaterial characterization is of significantly greater value than either of these alone. It is anticipated that the use and evolution of this approach will assist with the design and interpretation of studies assessing nanomaterial toxicity.

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 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)0.004
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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)0.0000.000

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 head0.159
GPT teacher head0.447
Teacher spread0.288 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations82
Published2010
Admission routes1
Has abstractyes

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