MétaCan
Menu
Back to cohort
Record W4409371806 · doi:10.1080/17425247.2025.2491642

Nanominerals: a multifaceted biomaterial for regenerative medicine and drug delivery

2025· editorial· en· W4409371806 on OpenAlexafffund
Aishik Chakraborty, Wei Luo, Amauri J. Paula

Bibliographic record

VenueExpert Opinion on Drug Delivery · 2025
Typeeditorial
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsWestern University
FundersInstitute of Musculoskeletal Health and ArthritisCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRegenerative medicineDrug deliveryBiomaterialDrugMedicineIntensive care medicinePharmacologyBiomedical engineeringNanotechnologyMaterials scienceStem cellBiologyCell biology

Abstract

fetched live from OpenAlex

Mineral-based nanoparticles, or nanominerals, can be defined as inorganic materials with at least one dimension ranging between 1 and 100 nm[1]. Nanominerals can be subdivided into three major clas...

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0120.011

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueExpert Opinion on Drug DeliverySame topicGraphene and Nanomaterials ApplicationsFrench-language works237,207