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Record W1947460837 · doi:10.24908/pceea.v0i0.3767

BIOMIMETICS AS PROBLEM-SOLVING, CREATIVITY AND INNOVATION TOOL

2011· article· en· W1947460837 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiomimeticsCreativityAbstractionVisualizationComputer scienceComponent (thermodynamics)Product (mathematics)Mathematics educationEngineering ethicsEngineeringArtificial intelligencePsychologyMathematicsEpistemology

Abstract

fetched live from OpenAlex

Engineering sketching, as taught in our first-year design course, exists somewhere between writing and formal drawing as a means of formulating ideas. In our third year of teaching engineering sketching assignments were given several additional components: the visualization of engineering concepts, sustainable product design and biomimetics. This was done for a number of reasons: Students were given the opportunity to integrate knowledge from other first year engineering courses; Students were challenged to think spatially, socially and philosophically (but not always in that order); Students who were not necessarily strong artists felt they could make up for this in the ‘additional component’ category; First year students seem to have a great interest in the study of structural biology as it applies to engineering design. Now in our fifth year, this paper discusses biomimetics, the abstraction of good design from nature, the transfer of technological ideas from nature to artificial applications, and the resulting student projects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.282
Teacher spread0.252 · 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