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Record W7011655674

MethodsJ2: A Software Tool to Improve Microscopy Methods Reporting [preprint]

2021· article· en· W7011655674 on OpenAlexfundno aff

Bibliographic record

VenueThe Journal of the American Medical Association (JAMA) Network (American Medical Association) · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthMcGill UniversityUniversity of MinnesotaIntellectual and Developmental Disabilities Research CenterSilicon Valley Community Foundation
KeywordsMetadataSoftwareSoftware toolSection (typography)Sample (material)
DOInot available

Abstract

fetched live from OpenAlex

Proper reporting of metadata is essential to reproduce microscopy experiments, interpret results and share images. Experimental scientists can report details about sample preparation and imaging conditions while imaging scientists have the expertise required to collect and report the image acquisition, hardware and software metadata information. MethodsJ2 is an ImageJ/Fiji based software tool that gathers metadata and automatically generates text for the methods section of publications.

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)0.041
metaresearch head score (Gemma)0.241
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.241
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.007
GPT teacher head0.343
Teacher spread0.336 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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