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Record W4401576663 · doi:10.1002/adom.202470072

Equal Rights for Activators – Ytterbium to Terbium Cooperative Sensitization in Molecular Upconversion (Advanced Optical Materials 23/2024)

2024· article· en· W4401576663 on OpenAlexaff
Federico Pini, Richard C. Knighton, Lohona K. Soro, Loı̈c J. Charbonnière, Marta Maria Natile, Niko Hildebrandt

Bibliographic record

VenueAdvanced Optical Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTerbiumYtterbiumPhoton upconversionMaterials scienceSensitizationOptoelectronicsOptical materialsNanotechnologyDopingLuminescence

Abstract

fetched live from OpenAlex

Understanding Molecular Upconversion In upconversion nanoparticles, the concentration of sensitizer ions is usually 9-fold higher than the activator ion concentration. In upconversion molecules, equal amounts of sensitizers and activators lead to brightest upconversion emission. In their Research Article (article number 2400423), Marta Maria Natile, Niko Hildebrandt, and co-workers apply advanced modeling to better understand the interaction of sensitizers and activators in nonanuclear lanthanide complexes.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.592
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.5920.305

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 head0.009
GPT teacher head0.270
Teacher spread0.260 · 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.

Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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