Multiangle Laser Light Scattering and Sedimentation Equilibrium
Bibliographic record
Abstract
Multiangle laser light scattering (MALLS) and sedimentation equilibrium are two powerful techniques used to characterize the association properties of proteins and their interactions in solution under physiological conditions. Both techniques have undergone a resurgence as a result of the advent of recombinant technologies which has enabled the generation of reasonable quantities of biologically significant proteins that exist in vivo in small amounts so that they can now be characterized physicochemically. As well, new technical developments with both techniques have made them much more sensitive and user friendly. In the case of static light scattering, this includes the use of lasers and modern detectors on-line with size exclusion chromatography so that one can establish absolute molecular weights of individual protein fractions eluting from the column. With sedimentation equilibrium, the Optima XL-I centrifuge (developed by Beckman, Palo Alto, CA) is equipped with both a new photoelectric scanning absorption optical system enabling exact measurement of concentration profiles at wavelengths of 190-800 nm and an interference optical system allowing the measurement of much higher concentration gradients. In both cases, powerful computer programs have been developed for data evaluation. Examples of the use of both techniques to study the association properties of cadherin in the presence and absence of calcium are described later. 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.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".