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Record W2332241662 · doi:10.1177/002029401004300205

Sensing in Nature: Using Biomimetics for Design of Sensors

2010· article· en· W2332241662 on OpenAlexaffabout
Torben Anker Lenau, Hyunmin Cheong, Shu Li

Bibliographic record

VenueMeasurement and Control · 2010
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiomimeticsComputer scienceIdentification (biology)Artificial intelligenceEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

The paper illustrates how biomimetics can be applied in sensor design. Biomimetics is an engineering discipline that uses nature as an inspiration source for generating ideas for how to solve engineering problems. Using biomimetics involves a search for relevant cases, a proper analysis of the biological solutions, identification of design principles and design of the desired artefact. We use a search method developed at University of Toronto. It is based on formulation of relevant keywords and search for occurrences in a standard university biology textbook. Most often a simple formulation of keywords and a following search is not enough to generate a sufficient amount of useful ideas or the search gives to many results. This is handled by a more advanced search strategy where the search is either widened or it is focused further mainly using biological synonyms. The paper also reviews a number of biomimetic studies of sense organs in animals.

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)0.001
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: none
Teacher disagreement score0.865
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.261
Teacher spread0.218 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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