Following the thread: <i>Mytilus</i> mussel byssus as an inspired multi-functional biomaterial
Bibliographic record
Abstract
Over the last 15 years, the byssus of marine mussels (Mytilus spp.) has emerged as an important model system for the bio-inspired development and synthesis of advanced polymers and adhesives. But how did these seemingly inconsequential fibers that are routinely discarded in mussel hors d’oeuvres become the focus of intense international research. In the present review, we take a historical perspective to understand this phenomenon. Our purpose is not to review the sizeable literature of mussel-inspired materials, as there are numerous excellent reviews that cover this topic in great depth. Instead, we explore how the byssus became a magnet for bio-inspired materials science, with a focus on the specific breakthroughs in the understanding of composition, structure, function, and formation of the byssus achieved through fundamental scientific investigation. Extracted principles have led to bio-inspired design of novel materials with both biomedical and technical applications, including surgical adhesives, self-healing polymers, tunable hydrogels, and even actuated composites. Continued study into the byssus of Mytilid mussels and other species will provide a rich source of inspiration for years to come.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".