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Record W2144374153 · doi:10.1109/isbi.2006.1624837

A Hybrid Data Analysis and Mesh Refinement Paradigm for Conformal Voxel Spectroscopy

2006· article· en· W2144374153 on OpenAlexaff
Charu Sharma, Lizann Bolinger, Lawrence Ryner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsNational Research Council CanadaNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsCuboidVoxelSIGNAL (programming language)Conformal mapComputer scienceSpectroscopyAlgorithmArtificial intelligencePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Recent advances in magnetic resonance spectroscopy have involved defining a conformal polyhedral shape around the tissue of interest (TOI). Up until now, magnetic resonance spectroscopy involved specifying a cuboid encompassing the volumetric shape of the TOI. The signal obtained from the cuboid includes signal from healthy tissue in addition to TOI, resulting in "contamination" of the total signal with non-TOI signal. Additional, a set of planes known as spatial saturation planes may be prescribed around the TOI so that a larger proportion of TOI to non-TOI signal is obtained. In this paper, we propose and implement an algorithm to optimize spatial saturation plane placement, resulting in greater proportion of signal from the TOI. This involves a better definition of the initial region of excitation using principal component analysis, followed by a mesh-based approach for plane placement

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.998

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)0.0030.000

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 head0.293
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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