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Creative Learning with Giant Triangles

2010· article· en· W24649435 on OpenAlex
Simon Morgan, Jacqueline Sack, Eva Knoll

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

VenueQuantitative Imaging in Medicine and Surgery · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicArt, Technology, and Culture
Canadian institutionsMount Saint Vincent University
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsMathematics educationEquilateral trianglePolyhedronArchitectureVocabularyDiscovery learningComputer scienceVisual artsPedagogyMathematicsPsychologyGeometryLinguisticsArtPhilosophy

Abstract

fetched live from OpenAlex

In this work, we propose and investigate a volume coil array design method using different types of birdcage coils for MR imaging. Unlike the conventional radiofrequency (RF) coil arrays of which the array elements are surface coils, the proposed volume coil array consists of a set of independent volume coils including a conventional birdcage coil, a transverse birdcage coil, and a helix birdcage coil. The magnetic fluxes of these three birdcage coils are intrinsically cancelled, yielding a highly decoupled volume coil array. In contrast to conventional non-array type volume coils, the volume coil array would be beneficial in improving MR signal-to-noise ratio (SNR) and also gain the capability of implementing parallel imaging. The volume coil array is evaluated at the ultrahigh field of 7T using FDTD numerical simulations, and the g-factor map at different acceleration rates was also calculated to investigate its parallel imaging performance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.288
Teacher spread0.244 · 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