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Record W2913161267 · doi:10.3390/app9040650

What Do We Learn from Good Practices of Biologically Inspired Design in Innovation?

2019· article· en· W2913161267 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

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCommercializationProcess (computing)Field (mathematics)Management scienceComputer scienceInnovation processKnowledge managementBusinessEngineeringMarketingWork in processMathematics

Abstract

fetched live from OpenAlex

Biologically inspired design (BID) is an emerging field of research with increasing achievements in engineering for design and problem solving. Its economic, societal, and ecological impact is considered to be significant. However, the number of existing products and success stories is still limited when compared to the knowledge that is available from biology and BID research. This article describes success factors for BID solutions, from the design process to the commercialization process, based on case studies and market analyses of biologically inspired products. Furthermore, the paper presents aspects of an effective knowledge transfer from science to industrial application, based on interviews with industrial partners. The accessibility of the methodological approach has led to promising advances in BID in practice. The findings can be used to increase the number of success stories by providing key steps toward the implementation and commercialization of BID products, and to point out necessary fields of cooperative research.

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)0.001
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 score0.409
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.070
GPT teacher head0.315
Teacher spread0.245 · 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