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Record W4248191080 · doi:10.1002/047134608x.w6605.pub2

Prosthetic Power Supplies

2015· other· en· W4248191080 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

VenueWiley Encyclopedia of Electrical and Electronics Engineering · 2015
Typeother
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaximum power transfer theoremWireless power transferBattery (electricity)Electrical engineeringEnergy harvestingComputer scienceWirelessPower managementPower (physics)Energy transferEngineeringTelecommunicationsEngineering physics

Abstract

fetched live from OpenAlex

During the last few decades, implantable medical devices (IMDs) changed the landscape of modern medicine. Combining many technologies and employing smart medical devices within the human body, they allowed a continuous and automatic management of numerous health issues, such as pacemakers and implantable cardiac defibrillators, cochlear implants, bladder controllers, endoscopic capsules, nerve stimulators, lab‐on‐a‐chip, and artificial retinal prosthesis. Due to their continuously increasing potential, IMDs are getting more complex, thus requiring more energy to operate. Most of these advanced implantable devices are extracorporeally powered or battery charged through wireless power transfer (WPT) mechanisms. Following the basic principle of IMD power supplies, we introduce various power transfer techniques, and then focus on the inductive links and various methods to maximize the energy transferred to implantable devices and the calibration methods of these WPT techniques.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.003
GPT teacher head0.170
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