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The Cytoscape Automation app article collection

2018· preprint· en· W2808741354 on OpenAlex

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

VenueF1000Research · 2018
Typepreprint
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Human Genome Research Institute
KeywordsOpen peer reviewPlant biologyAutomationNeuroscienceComputational biologyPhysiologyBiologyMedicineData scienceBioinformaticsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.

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)0.029
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0050.000
Open science0.0040.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.009

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 head0.230
GPT teacher head0.473
Teacher spread0.243 · 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