MétaCan
Menu
Back to cohort
Record W4246378939 · doi:10.32920/ryerson.14640912.v1

Advancing biomimetic materials through ISO standards

2021· preprint· en· W4246378939 on OpenAlexaff
Norbert Hoeller, Filippo A. Salustri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBiomimeticsStandardizationConsistency (knowledge bases)Function (biology)Quality (philosophy)International standardizationEngineeringProductivityEngineering managementBusinessSystems engineeringManagement scienceComputer scienceNanotechnologyEngineering ethicsProcess managementEconomicsArtificial intelligenceMaterials science

Abstract

fetched live from OpenAlex

This paper discusses the challenges and opportunities of developing standards for biomimetic materials, based on the authors experience with International Organization for Standardization (ISO)/Technical Committee 266 Biomimetics. With the expansion of global trade, international standards are increasingly called on to protect the interests of consumers, improve business productivity and facilitate trade. In the past, standards typically addressed form/fit/function specifications and were associated with mature industries. Recently some ISO standards are focusing on processes, quality and consistency, which can support advances in emerging fields. ISO has the potential to advance biomimetic materials and biomimetics in general by developing and promoting frameworks that reflect the evolving nature of biomimetics.

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)0.045
metaresearch head score (Gemma)0.052
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: Methods · Consensus signal: Methods
Teacher disagreement score0.045
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.004
Scholarly communication0.0090.013
Open science0.0040.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.007

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 head0.008
GPT teacher head0.244
Teacher spread0.235 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicManufacturing Process and OptimizationFrench-language works237,207