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Record W3013644324 · doi:10.1039/d0an00450b

Quantitative viability detection for a single microalgae cell by two-level photoexcitation

2020· article· en· W3013644324 on OpenAlexafffund
Gege Ding, Junsheng Wang, Lanlan Wang, Jiang Zou, Peitao Tian, Yichi Zhang, Xinxiang Pan, Dongqing Li

Bibliographic record

VenueThe Analyst · 2020
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Liaoning ProvinceNational Natural Science Foundation of China
KeywordsPhotoexcitationViability assayChemistryBiological systemCellBiologyPhysicsBiochemistryExcited stateAtomic physics

Abstract

fetched live from OpenAlex

has been used to characterize the viability of single microalgae cells during cell migration. By using the relative fluorescence yield ratio, this method does not require the intensity of the excitation light to be very low for the assessment of the fluorescence yield of a dark-adapted microalgae cell, nor to be very strong to reach the saturated light level to assess the maximum fluorescence yield. Therefore, this method greatly reduces the technical difficulties of developing a sensor device. Well balanced portability, accuracy and universal applicability make it suitable for on-site real-time detection.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.051
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

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

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 head0.163
GPT teacher head0.344
Teacher spread0.181 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2020
Admission routes2
Has abstractyes

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