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Record W4231545666 · doi:10.1002/ppap.201870023

Issue Information: Plasma Process. Polym. 10/2018

2018· paratext· en· W4231545666 on OpenAlexaff
Sophia Wang, Zhongfan Liu, Mianshui Rong, Michael G. Kong, Sten-Mark Kretzschmar, Andreas Pfuch, Oliver Beier, Mario Beyer, Bernd Grünler, Renate Förch, Regina Hagen, Katja Kornmacher, Amanda M. Smith, Pietro Favia, M. R. Wertheimer, Dirk Hegemann

Bibliographic record

VenuePlasma Processes and Polymers · 2018
Typeparatext
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPlasmaJet (fluid)Process (computing)Materials scienceMechanicsPhysicsComputer scienceNuclear physics

Abstract

fetched live from OpenAlex

A model tissue was treated by a He+O 2 plasma jet in this study, in which the spatial distribution of ROS was found to change signifi cantly with the inclination angle of plasma irradiation, while the plasma image was almost the same. Since the inclination angle should be changeable in practice, this suggests that the ROS dosage of the plasma jet is diffi cult to precisely control for clinical applications. Further details can be found in the article by Dingxin Liu, Tongtong He, Zhijie Liu, et al. (e1800057)

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 categoriesMeta-epidemiology (narrow), 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0200.020

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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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
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
Published2018
Admission routes1
Has abstractyes

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