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Record W4412570668 · doi:10.61092/iaea.h1w5-dtyy

Development of Java GAMUT (JGAMUT) – Adopted Levels, Gammas Evaluator Assistant Code

2016· report· en· W4412570668 on OpenAlexaff
M.J. Birch

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGamutJavaComputer scienceCode (set theory)Programming languageOperating systemComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

The Java GAMUT (JGAMUT) code is designed to be a tool which assists evaluators in producing Adopted Levels, Gammas (ALG) datasets.In particular JGAMUT reduces the amount of tedious work performed by the evaluator; provides routines for systematically correcting discrepant data, which previously was done inconsistently between evaluators; provides more sophisticated statistical methods for obtaining adopted gamma-ray energies and intensities.This code includes a gamma-by-gamma routine, which essentially automates the weighted averaging process originally done by evaluators by hand, as well as the GAMUT routines which use the algorithms of the original GAMUT code to produce more statistically sound adopted energies and intensities.Details regarding the algorithms and usage of this code are contained in this document for the reference of the users.

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), 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: none
Teacher disagreement score0.595
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)0.0040.000

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 head0.079
GPT teacher head0.319
Teacher spread0.240 · 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
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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