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Record W2149860372 · doi:10.1109/iros.2009.5353964

A robotic micro-assembly process inspired by the construction of the ancient pyramids and relying on several thousand flagellated bacteria acting as micro-workers

2009· article· en· W2149860372 on OpenAlexaff
Sylvain Martel, Mahmood Mohammadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPropulsionMagnetotactic bacteriaFlagellumProcess (computing)RobotBacteriaRoboticsComputer scienceNanotechnologyEngineeringArtificial intelligenceControl engineeringBiochemical engineeringAerospace engineeringBiologyMaterials science

Abstract

fetched live from OpenAlex

Bacteria can be used as computer-controlled bioactuators and means of propulsion for microrobots and other micro-scaled entities to accomplish precise operations as first proposed by our research group in [1] and demonstrated later experimentally in [2]. The last reference confirmed that the propulsion force provided by the flagella being connected to molecular motors embedded in the bacterial cell, could be exploited to replace more conventional technologies being presently used in robotics but which could not be implemented at such a scale. In a coherent effort, our group also pioneered a method of not only harnessing instead of mimicking nature by using flagellated bacteria and more specifically Magnetotactic Bacteria (MTB) for propulsion and transport, but also for the controlled steering or computerized directional swimming control of bacterial micronanorobots [3].

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.227
Teacher spread0.220 · 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 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

Citations14
Published2009
Admission routes1
Has abstractyes

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