MétaCan
Menu
Back to cohort

Permanent magnet motor drive technology for mitigating greenhouse gas emissions

2024· article· en· W4396675458 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

VenueApplied and Computational Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsSte. Anne's Hospital
Fundersnot available
KeywordsGreenhouse gasAutomotive industryClimate changeClimate change mitigationEnvironmental scienceGlobal warmingNatural resource economicsBusinessEngineeringEconomicsEcologyAerospace engineering

Abstract

fetched live from OpenAlex

Global climate change, resulting from the release of greenhouse gases, poses a severe threat to human existence. The detrimental consequences of this phenomenon encompass rising sea levels, modified climate zones, and intensified extreme weather events. To counteract greenhouse gas emissions, the development of permanent magnet motor drive technology has gained prominence, especially in the automotive industry. This comprehensive review examines the advantages, limitations, and potential applications of permanent magnet motor drives in mitigating the adverse effects of greenhouse gases. Furthermore, the review addresses the challenges associated with this technology and outlines future research and development directions in this field. The findings of this review provide valuable insights into the capacity of permanent magnet motor drives to combat climate change and pave the way for future advancements in this critical domain. By adopting and advancing this technology, we can strive towards a more sustainable future and alleviate the threats posed by global climate change.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.695

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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