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Record W4283800323 · doi:10.1002/ppap.202270019

Inside Front Cover: Plasma Process. Polym. 7/2022

2022· paratext· en· W4283800323 on OpenAlexaff
Gabriel Morand, Pascale Chevallier, Michaël Tatoulian, Diego Mantovani

Bibliographic record

VenuePlasma Processes and Polymers · 2022
Typeparatext
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFront coverMaterials scienceNanotechnologyPlasmaCover (algebra)Chemical engineeringProcess engineeringPolymer scienceMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Inside Front Cover: Open-air plasma is becoming an effective alternative to deposit coatings while entrapping bioactive macromolecules for biomedical applications. Thanks to the use of an innovative aerosol introduction strategy, composite agentdelivery biodegradable coatings were directly deposited. This process allowed to control the kinetic of agents release in water as a function of the structure of the precursors. The proposed approach provides new insight to the efficient deposition of controlled kinetics drug-eluting coatings. Further details can be found in the article by Gabriel Morand, Pascale Chevallier, Michael Tatoulian, and Diego Mantovani (e2100252)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.387
Threshold uncertainty score0.553

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6130.325

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 head0.262
Teacher spread0.248 · 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; the direct Gemma label and the distilled Codex classifier 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

Citations1
Published2022
Admission routes1
Has abstractyes

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