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Record W2552713324 · doi:10.1109/mpot.2016.2549998

Energy Harvesting From the Human Body for Biomedical Applications

2016· article· en· W2552713324 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

VenueIEEE Potentials · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnergy harvestingEnergy (signal processing)Electric potential energyThermal energyEnergy sourceElectrical engineeringAvailable energyRenewable energyWind powerEngineeringComputer scienceEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

Energy harvesting has been growing in demand, and there is a variety of ongoing research to discover techniques for supplying electronic devices with a lifetime of power. The process of energy harvesting requires an energy source for harvesting and an energy harvesting sensor that matches that type of energy source. An energy harvester is a small device that converts available energy from the surrounding environment into electrical energy that can then be used to power small devices. Typical sources of energy include electromagnetic, wind, vibrational, and thermal energy.

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 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 score0.938
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.022
GPT teacher head0.251
Teacher spread0.230 · 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