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.
Bibliographic record
Abstract
Molecular translational self-diffusion, a measure of diffusive motion, provides information on the effective molecular hydrodynamic radius, as well as information on the properties of media or solution through which the molecule diffuses. Protein translational diffusion measured by pulsed-field gradient nuclear magnetic resonance (PFG-NMR) has seen increased application in structure and interaction studies, as structural changes or protein-protein interactions are often accompanied by alteration of their effective hydrodynamic radii. Unlike the analysis of complex mixtures by PFG-NMR, for monitoring changes of protein translational diffusion under various conditions, such as different stages of folding/unfolding, a partial region of the spectrum or even a single resonance is sufficient. We report translational diffusion coefficients measured by PFG-NMR with a modified stimulated echo (STE) sequence where band-selective pulses are employed for all three (1)H RF pulses. Compared with conventional non-selective sequence, e.g. the BPP-LED sequence, the advantage of this modified band-selective excitation short transient (BEST) version of STE (BEST-STE) sequence is multi-fold, namely: (1) potential sensitivity gain as in generalized BEST-based sequences, (2) water suppression is no longer required as the magnetization of solvent water is not perturbed during the measurement, and (3) dynamic range problems due to the presence of intense resonances from molecules other than the protein or peptide of interest, such as non-deuterated detergent micelles, are avoided.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 it