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Record W4310447605 · doi:10.1142/13282

Numerical Modeling of Superconducting Applications

2022· book· en· W4310447605 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

VenueWORLD SCIENTIFIC eBooks · 2022
Typebook
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSuperconductivityComputer scienceStatistical physicsPhysicsNuclear engineeringEngineering physicsEngineeringCondensed matter physics

Abstract

fetched live from OpenAlex

Superconductors enable many large-scale electric applications, both current and under research, with a high potential to cause important breakthroughs in human development.These are, for example, the reduction of emissions responsible for the climate crisis through energy generation (fusion and offshore wind turbines), electric transportation (electric and hybrid-electric airplanes or sea vessels), and energy-efficient electric networks (power-transmission cables and transformers).Superconductors also enable novel medical instruments, such as (high-field) magnetic resonance imaging (MRI) and accelerators for ion cancer therapy.Last but not least, superconducting magnets made it possible to conduct some of the largest experiments in fundamental research in the world, involving particle accelerators and detectors, such as the large hadron collider (LHC).

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.036
GPT teacher head0.236
Teacher spread0.200 · 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