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Record W4284666685 · doi:10.3390/biomimetics7030093

The Education Pipeline of Biomimetics and Its Challenges

2022· article· en· W4284666685 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

VenueBiomimetics · 2022
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of CalgaryUniversity of Guelph
Fundersnot available
KeywordsBiomimeticsPipeline (software)Engineering ethicsCurriculumEngineeringEngineering managementComputer scienceSociologyMechanical engineeringArtificial intelligencePedagogy

Abstract

fetched live from OpenAlex

Biomimetics must be taught to the next generation of designers in the interest of delivering solutions for current problems. Teaching biomimetics involves teachers and students from and in various disciplines at different stages of the educational system. There is no common understanding of how and what to teach in the different phases of the educational pipeline. This manuscript describes different perspectives, expectations, needs, and challenges of users from various backgrounds. It focuses on how biomimetics is taught at the various stages of education and career: from K-12 to higher education to continuing education. By constructing the biomimetics education pipeline, we find that some industry challenges are addressed and provide opportunities to transfer the lessons to application. We also identify existing gaps in the biomimetics education pipeline that could further advance industry application if a curriculum is developed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.304

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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