MétaCan
Menu
Back to cohort
Record W4231124763 · doi:10.36227/techrxiv.15125520

Low Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap

2021· preprint· en· W4231124763 on OpenAlexaff
Mounir Laroussi, Sander Bekeschus, Annemie Bogaerts, Alexander Fridman, Xinpei Lu, Vandana Miller, Christophe O. Laux, James A. Walsh, Chunqi Jiang, Dawei Liu, Maksud Yusupov, Michael Keidar, Masaru Hori, Katharina Stapelmann, Ali Mesbah, Selma Mededovic Thagard, Hiromasa Tanaka, Dayuan Yan, Kostya Ostrikov, Stephan Reuter

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsPolytechnique Montréal
FundersVlaamse regeringFonds Wetenschappelijk Onderzoek
KeywordsField (mathematics)Engineering ethicsPerspective (graphical)Plasma medicineNanotechnologyComputer scienceEngineering physicsAtmospheric-pressure plasmaEngineeringPhysicsPlasmaMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

<p>Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The introduction of low temperature, atmospheric pressure plasmas to the biomedical field has ushered a new revolution in the healthcare arena that promises to introduce plasma-based therapies to combat some thorny and long-standing medical challenges. This paper presents an overview of where research is at today and discusses innovative concepts and approaches to overcome present challenges and take the field to the next level. It is written by a team of experts who took an in-depth look at the various biomedical applications, made critical analysis, and proposed ideas and concepts that should help the research community focus their efforts on clear and practical steps necessary to keep the field advancing for decades to come. </p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.017
GPT teacher head0.319
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicPlasma Applications and DiagnosticsFrench-language works237,207