Bibliographic record
Abstract
The changes that sediments undergo after deposition are collectively known as diagenesis. Diagenesis is not widely recognized as a source for mathematical ideas; however, the myriad processes responsible for these changes lead to a wide variety of mathematical models. In fact, most of the classical models and methods of applied mathematics emerge naturally from quantification of diagenesis. For example, small‐scale sediment mixing by bottom‐dwelling animals can be described by the diffusion equation; the dissolution of biogenic opal in sediments leads to sets of coupled, nonlinear, ordinary differential equations; and modeling organisms that eat at depth in the sediment and defecate at the surface suggests the one‐dimensional wave equation, while the effect of waves on pore waters is governed by the two‐ or three‐dimensional wave equation. Diagenetic modeling, however, is not restricted to classical methods. Diagenetic problems of concern to modern mathematics exist in abundance; these include free‐boundary problems that predict the depth of biological mixing or the penetration of O2 into sediments, algebraic‐differential equations that result from the fast‐reversible reactions that regulate pH in pore waters, inverse calculations of input functions (histories), and the determination of the optimum choice in a hierarchy of possible diagenetic models. This review highlights and explores these topics with the hope of encouraging further modeling and analysis of diagenetic phenomena.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".