A novel approach to investigate the deposition of (bio)chemical sediments: The sedimentation velocity of cyanobacteria–ferrihydrite aggregates
Bibliographic record
Abstract
ABSTRACT Sedimentation velocities of various chemical sediments are typically calculated using Stokes's law. However, applying it to chemical sediments that form in situ in the water column is not ideal because the particle properties do not fulfill many of the assumptions underpinning the applicability of Stokes' law. As a consequence, it has been difficult to predict the sedimentation rate of ancient chemical sediments, such as Precambrian banded iron formations (BIF), because their primary sediments likely comprised aggregates of ferric hydroxides, such as ferrihydrite [Fe(OH)3], and marine bacterial biomass, including cyanobacteria. In this work we use a new experimental method to address the mechanisms by which primary BIF sediment, formed by the oxidation of dissolved Fe(II) by O2 and simultaneously incubated with cyanobacterium Synechococcus sp., were deposited to the Archean ocean. Specifically, we formed the aggregates in situ over a wide range of initial pH and Fe(II) concentrations, continuously recorded the entire settling processes of aggregates under each condition, and then processed the data in MATLAB according to different settling mechanisms. Our results demonstrate that ferrihydrite–cyanobacteria aggregates settled to the ocean floor either through the formation of uniformly descending concentration fronts or through convective plumes. The sedimentation mechanism depended on both initial Fe(II) concentration and the pH. Correspondingly, two algorithms were developed to characterize the sedimentation velocity. These algorithms tracked the alteration of light intensity from low to high as sediments descended from an initially homogeneous state through a water tank, and as well calculated the average light intensity over time, from which vertical time series were constructed allowing calculation of the sedimentation velocity. Our method not only provides an accurate estimation of the in situ sedimentation velocity of cell–mineral aggregates, but also provides new insights into the physical mechanisms by which the primary sediments composing BIF were deposited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".