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Record W3158845648 · doi:10.2110/jsr.2020.114

A novel approach to investigate the deposition of (bio)chemical sediments: The sedimentation velocity of cyanobacteria–ferrihydrite aggregates

2021· article· en· W3158845648 on OpenAlexaff
Yuhao Li, Bruce Sutherland, Murray K. Gingras, George W. Owttrim, Kurt O. Konhauser

Bibliographic record

VenueJournal of Sedimentary Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFerrihydriteSedimentationSettlingSedimentGeologyMineralogyEnvironmental chemistryChemistryAdsorptionEnvironmental scienceGeomorphology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.054
GPT teacher head0.290
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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