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Record W4312690903 · doi:10.1109/pn56061.2022.9908342

Single-shot phosphorescence lifetime imaging thermometry for wide-field temperature sensing

2022· article· en· W4312690903 on OpenAlexaff
Xianglei Liu, Artiom Skripka, Yingming Lai, Cheng Jiang, Jingdan Liu, Fiorenzo Vetrone, Jinyang Liang

Bibliographic record

Venue2022 Photonics North (PN) · 2022
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhotoluminescencePhosphorescenceSingle shotMaterials scienceTemperature measurementOptoelectronicsField (mathematics)OpticsFluorescencePhysics

Abstract

fetched live from OpenAlex

Photoluminescence lifetime imaging of upconverting nanoparticles (UCNPs) is useful for optical thermometry but has not yet been implemented in wide-field temperature sensing in a single exposure. To overcome this limitation, we have developed single-shot photoluminescence lifetime imaging thermometry (SPLIT). Using the core/shell structured UCNPs as the temperature indicators, we apply SPLIT to wide-field temperature mapping of a dynamic biological sample at video rate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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