Studying natural structural protein fibers by solid‐state nuclear magnetic resonance
Bibliographic record
Abstract
Abstract As a consequence of evolutionary pressure, various organisms have developed structural fibers displaying a range of exceptional mechanical properties adapted specifically to their functions. An understanding of these properties at the molecular level requires a detailed description of local structure, orientation with respect to the fiber and size of constitutive units, and dynamics on various timescales. The size and lack of long‐range order in these protein systems constitute an important challenge to classical structural techniques such as high‐resolution NMR and X‐ray diffraction. Solid‐state NMR overcomes these constraints and is uniquely suited to the study of these inherently disordered systems. Solid‐state NMR experiments developed or applied to determine structure, orientation, and dynamics of these complex proteins will be reviewed and illustrated through examples of their applications to fibers such as spider and silkworm silks, collagen, elastin, and keratin. © 2009 Wiley Periodicals, Inc. Concepts Magn Reson Part A 34A: 24–27, 2009.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 0.000 |
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.
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".