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Record W2232521377 · doi:10.3109/0284186x.2015.1067718

Development of a method for functional aspect identification in parotid using dynamic contrast-enhanced magnetic resonance imaging and concurrent stimulation

2015· letter· en· W2232521377 on OpenAlexaffabout
Haley Clark, Vitali Moiseenko, Thomas Rackley, Steven Thomas, Jonn Wu, Stefan A. Reinsberg

Bibliographic record

VenueActa Oncologica · 2015
Typeletter
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsMedicineMagnetic resonance imagingDynamic contrastDynamic contrast-enhanced MRIStimulationNuclear magnetic resonanceContrast (vision)Functional magnetic resonance imagingFunctional imagingNuclear medicineRadiologyInternal medicineArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

1Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada, 2Department of Medical Physics, British Columbia Cancer Agency, Vancouver, British Columbia, Canada, 3Department of Medicine and Applied Sciences, University of California, San Diego, La Jolla, California, USA and 4Department of Radiation Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.602
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.075
GPT teacher head0.376
Teacher spread0.301 · 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 designNot applicable
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

Citations5
Published2015
Admission routes2
Has abstractyes

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