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Record W2113816719 · doi:10.1039/b907749a

Genetically encoded biosensors based on engineered fluorescent proteins

2009· review· en· W2113816719 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemical Society Reviews · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthCanada Research Chairs
KeywordsFluorescent proteinBiosensorFluorescenceGreen fluorescent proteinLive cell imagingChemistryNanotechnologyCell biologyBiophysicsComputational biologyBiochemistryBiologyCellGeneMaterials science

Abstract

fetched live from OpenAlex

Fluorescent proteins have revolutionized cell biology by allowing researchers to non-invasively peer into the inner workings of cells and organisms. While the most common applications of fluorescent proteins are to image expression, localization, and dynamics of protein chimeras, there is a growing interest in using fluorescent proteins to create biosensors for minimally invasive imaging of concentrations of ions and small molecules, the activity of enzymes, and changes in the conformation of proteins in living cells. This tutorial review provides an overview of the progress made in the development of fluorescent protein-based biosensors to date.

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.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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