Bibliographic record
Abstract
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".