Biostabilization: Parameterizing the interactions between microorganisms and siliciclastic marine sediments
Bibliographic record
Abstract
Microbial mats have existed for much of Earth's history. They represent some of the earliest evidence of life, are essential in biogeochemical cycles, and played a pivotal role in oxygenating the atmosphere. In addition, benthic microbiota impact sediment properties by enhancing the cohesion and stability of the substratum, a process known as ‘biostabilization’, which affects sediment dynamics and rheology. A substantial body of research has focused on experimentally quantifying biostabilization in siliciclastic sediments. This review compiles and synthesizes these studies in order to facilitate comparison of results. They, in turn, are compared with; (1) the Shields' diagram, (2) shear stress values in shallow marine environments, and (3) occurrences of microbially induced sedimentary structures in the marine stratigraphic record. The findings reveal significant variability in outcomes, with increases in the Shields' Parameter ranging from 0.1 to 4 orders of magnitude. They also demonstrate that high-energy hydrodynamic conditions, such as those above fairweather wave base, inhibit microbial colonization. Additionally, the review briefly discusses two applications of the data: (1) refining models of the Great Oxidation Event, and (2) evaluating microbial biostabilization as a response to increased coastal erosion driven by climate change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".