Forced Biomineralization: A Review
Bibliographic record
Abstract
Biologically induced and controlled mineralization of metals promotes the development of protective structures to shield cells from thermal, chemical, and ultraviolet stresses. Metal biomineralization is widely considered to have been relevant for the survival of life in the environmental conditions of ancient terrestrial oceans. Similar behavior is seen among extremophilic biomineralizers today, which have evolved to inhabit a variety of industrial aqueous environments with elevated metal concentrations. As an example of extreme biomineralization, we introduce the category of "forced biomineralization", which we use to refer to the biologically mediated sequestration of dissolved metals and metalloids into minerals. We discuss forced mineralization as it is known to be carried out by a variety of organisms, including polyextremophiles in a range of psychrophilic, thermophilic, anaerobic, alkaliphilic, acidophilic, and halophilic conditions, as well as in environments with very high or toxic metal ion concentrations. While much additional work lies ahead to characterize the various pathways by which these biominerals form, forced biomineralization has been shown to provide insights for the progression of extreme biomimetics, allowing for promising new forays into creating the next generation of composites using organic-templating approaches under biologically extreme laboratory conditions relevant to a wide range of industrial conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".