Bibliographic record
Abstract
While biological apatite (bone mineral) resorption is understood, from the perspective of crystallization theory, nucleation is not. The degree of saturation (Ω) describes the chemical driving force for mineral dissolution (Ω <1) or formation (Ω > 1). Ω is the ratio of the ion activity product (IAP) of free apatite (available for reaction) component ion concentrations (predominately [Ca²⁺] and [PO¾⁻)] for apatite) and its solubility product (K(sp)). Free ion concentrations can be less than total ion concentrations if the ions form complexes, or if the ion speciation changes. Within the acidic bone resorption pit, free [PO¾⁻] is reduced due to speciation into H₂PO₄⁻. This reduces IAP(bio-Ap), and Ω(bio-Ap); at Ω(bio-Ap) <1, apatite dissolves. Apatite nucleation requires Ω(bio-Ap) >1, and bioaccumulation of molar total [Ca²⁺] and [PO¾⁻] to form the 60-70 weight percent mineral in bone tissue. This is possible with the polymerization of PO¾⁻ into polyphosphate [polyP: (PO₃⁻)(n)] which reduces free [PO¾⁻] while leaving total [P] unchanged. polyP forms neutral complexes by chelation with Ca²⁺, which further reduces free [Ca²⁺] and Ω(bio-Ap), yet total [Ca²⁺] is unchanged. In vitro experiments demonstrate reduction in free [Ca²⁺], free [PO¾⁻], and Ω(bio-Ap) by Ca-polyP formation, while total [Ca²⁺] and total [P] are constant. polyP depolymerization restores free [Ca²⁺] and [PO¾⁻] to total [Ca] and [P], and increases Ω(bio-Ap), favouring apatite nucleation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".