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Record W2225662698 · doi:10.1385/1-59259-184-1:075

Steady-State Fluorescence Spectroscopy

2003· article· en· W2225662698 on OpenAlexafffund
Aalim M. Weljie, Hans J. Vogel

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of Calgary
FundersAlberta Heritage Foundation for Medical Research
KeywordsFluorescenceTryptophanFluorescence spectroscopyFluorescence correlation spectroscopyChemistryBiological systemBiophysicsFluorescence in the life sciencesComputer scienceComputational biologyNanotechnologyBiochemistryMaterials scienceBiologyPhysicsAmino acidOptics

Abstract

fetched live from OpenAlex

Fluorescence spectroscopy has long been a popular method for protein studies from which researchers have garnered a wealth of biophysical information (1,2). Several specific fluorescence methods have been recently well reviewed (3–5) and readers are encouraged to seek out these references for theory and methods complimentary to those presented in this chapter. The basic selling features for the general use of this tool in biological systems include the relatively low concentrations of sample material required, the occurrence of natural fluorophores in proteins such as tryptophan and tyrosine, the breadth of fluorescence experiments available, and the comparatively simple (and inexpensive) equipment required for most experiments. It is no surprise then that the literature is replete with examples of calcium-binding proteins which have in one way or another been characterized by some fluorescence method. Information available to the researcher includes, but is not limited to, biochemical characteristics such as conformational changes, protein-protein interactions, metal-binding information, membrane localization, long-range distance measurements, and kinetic/dynamic parameters. The majority of this chapter will concentrate on protocols for simple steady-state single-tryptophan fluorescence measurements to probe protein-peptide interactions. References to other fluorescence methods and applications will also be provided.

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 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 score0.332
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.257
Teacher spread0.231 · 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

Citations7
Published2003
Admission routes2
Has abstractyes

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