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Record W3107001797 · doi:10.3390/pharmaceutics12121153

Erratum: Gascon S.; et al. Characterization and Mathematical Modeling of Alginate/Chitosan-Based Nanoparticles Releasing the Chemokine CXCL12 to Attract Glioblastoma Cells. Pharmaceutics 2020, 12, 356

2020· erratum· en· W3107001797 on OpenAlexaff
Suzanne Gascon, Angéla Giraldo Solano, Wiam El Kheir, Hélène Therriault, Pierre Berthelin, Bettina Cattier, Bernard Marcos, Nick Virgilio, Benoît Paquette, Nathalie Faucheux, Marc-Antoine Lauzon

Bibliographic record

VenuePharmaceutics · 2020
Typeerratum
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsPolytechnique MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsPharmaceuticsGlioblastomaChitosanCharacterization (materials science)ChemokineMaterials scienceChemistryCancer researchNanotechnologyMedicinePharmacologyBiochemistry

Abstract

fetched live from OpenAlex

The authors wish to make the following correction to this paper [...].

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)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0040.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0180.017

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 head0.039
GPT teacher head0.308
Teacher spread0.269 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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