Modeling the calcium and phosphate mineralization of American lobster cuticle
Bibliographic record
Abstract
Bottom-up modeling of American lobster (Homarus americanus) cuticle explains architecture and function ab initio, from first principles, starting with synthesis of component polymers and progressively building composite structure that should explain observed properties. A top-down perspective decomposes the lobster cuticle starting at the top level of structural complexity and function aiming to descend to the finest detail. Both approaches aim to ultimately model the same cuticle structure. Current bottom-up models of the cuticle do not succeed in explaining key structural and functional detail identified by top-down approaches. Top-down identified structures and associated functions are valuable as bases for potential vulnerabilities to microbial attack. An immediate objective is to inform the bottom-up approach of top-down identified model components critical to cuticle function. Top-down features include detail of protein expression and mineral heterogeneity and their function in observed structures. This function-directed approach provides a better understanding of the distribution and roles of minerals in relation to their immediate cuticle environment. The top-down identified features can hopefully be included in ab initio models to improve our understanding of cuticle design.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".