Bibliographic record
Abstract
Over the last eight years new theories regarding nucleation, crystal growth, and polymorphism have emerged. Many of these theories were developed in response to observations in nature, where classical nucleation theory failed to account for amorphous mineral precursors, phases, and particle assembly processes that are responsible for the formation of invertebrate mineralized skeletal elements, such as the mollusk shell nacre layer (aragonite polymorph) and the sea urchin spicule (calcite polymorph). Here, we summarize these existing nucleation theories and place them within the context of what we know about biomineralization proteins, which are likely participants in the management of mineral precursor formation, stabilization, and assembly into polymorphs. With few exceptions, much of the protein literature confirms that polymorph-specific proteins, such as those from mollusk shell nacre aragonite, can promote polymorph formation. However, past studies fail to provide important mechanistic insights into this process, owing to variations in techniques, methodologies, and the lack of standardization in mineral assay experimentation. We propose that the way forward past this roadblock is for the protein community to adopt standardized nucleation assays and approaches that are compatible with current and emerging nucleation precursor studies. This will allow cross-comparisons, kinetic observations, and hopefully provide the information that will explain how proteins manage polymorph formation and stabilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".