MétaCan
Menu
Back to cohort
Record W1579678587 · doi:10.1109/intmag.2015.7156893

A frequency up-converted magnetostrictive transducer for harvesting energy from finger tapping

2015· article· en· W1579678587 on OpenAlex
Zhengbao Yang, Ya Tan, Jean W. Zu

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

Venue2015 IEEE Magnetics Conference (INTERMAG) · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransducerMagnetostrictionExcitationAcousticsTappingVibrationMaterials scienceEnergy harvestingEnergy (signal processing)Magnetic fieldFinger tappingElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

We developed a new Galfenol-based MEH using frequency up-conversion. The importance of high vibration frequency and bias magnetic field is emphasized and taken full advantage of in the proposed MEH. The fabricated prototype is capable of harvesting energy from mechanical motions and oscillations regardless of the frequency or velocity of the excitation source. Both mechanical and electrical responses of the prototype have been analyzed. The study is not complete. We are optimizing the design, examining the responses of the MEH under different excitation frequencies and strength, and exploring the effect of external electric loads.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.251
Teacher spread0.190 · 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