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Record W3109969363 · doi:10.1002/adma.202070353

Disease Prediction: Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium (Adv. Mater. 47/2020)

2020· article· en· W3109969363 on OpenAlexaff
Hana Kokot, Boštjan Kokot, Aleksandar Sebastijanović, Carola Voss, Rok Podlipec, Patrycja Zawilska, Trine Berthing, Carolina Ballester‐López, Pernille Høgh Danielsen, Claudia Contini, Mikhail Ivanov, Ana Krišelj, Petra Čotar, Qiaoxia Zhou, Jessica Ponti, Vadim Zhernovkov, M. Schneemilch, Zahra Doumandji, Mojca Pušnik, Polona Umek, Stane Pajk, Olivier Joubert, Otmar Schmid, Iztok Urbančič, Martin Irmler, Johannes Beckers, Vladimir Lobaskin, Sabina Halappanavar, Alexander P. Lyubartsev, Ulla Vogel, Tilen Koklič, Tobias Stoeger, Janez Štrancar

Bibliographic record

VenueAdvanced Materials · 2020
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsHealth Canada
Fundersnot available
KeywordsInflammationIn vivoNanomaterialsMaterials scienceCyclingNanotechnologyNanotoxicologyBiologyImmunologyNanoparticle

Abstract

fetched live from OpenAlex

The prediction of diseases associated with nanomaterials is currently hampered by an incomplete understanding of the underlying mechanisms. Newly discovered nanomaterial quarantining and counteracting nanomaterial cycling fill that gap allowing Tilen Koklič, Tobias Stoeger, Janez Štrancar, and co-workers to incorporate these main modes of cellular response into a mechanistic model and predict in vivo inflammation solely on animal-free in vitro tests, as described in article number 2003913.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.291
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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