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Record W2165921504 · doi:10.1364/ao.45.003495

Ferrofluidic adaptive mirrors

2006· article· en· W2165921504 on OpenAlexaff
Philip Laird, N. Caron, Marc Rioux, E. F. Borra, Anna M. Ritcey

Bibliographic record

VenueApplied Optics · 2006
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsActuatorDeformable mirrorComputer scienceOpticsScalabilityPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

A magnetic liquid mirror based on ferrofluids was demonstrated. Magnetic liquid mirrors represent a major departure from solid mirror technology. They present both advantages and disadvantages with respect to established technologies. Stroke (from a fraction of a wave to several hundreds of micrometers), cost (a few dollars per actuator), and scalability (hundreds of thousands of actuators) are the main advantages. Very large mirrors having diameters of the order of a meter should be feasible. There are a few disadvantages. The most important disadvantage is the time response, which is of the order of a few milliseconds. Although this time response could be further decreased with additional technical developments, it is unlikely to match the speed of solid mirrors. The technology is still in its infancy, and considerable work must still be done. However, the advantages are such that the technology is worth pursuing.

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: Empirical
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.176
Teacher spread0.167 · 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

Citations28
Published2006
Admission routes1
Has abstractyes

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