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Record W2102608380 · doi:10.1666/05077.1a

Growth modes of 2-Ga microfossils

2007· article· en· W2102608380 on OpenAlexaff
Steven Bennett, David H. Boal, Hanna Ruotsalainen

Bibliographic record

VenuePaleobiology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsExponential growthVolume (thermodynamics)Plane (geometry)Cell divisionGrowth rateDivision (mathematics)Exponential functionLinear growthDoubling timeExponential distributionSurface (topology)GeometryBiologyGeologyCellPhysicsMathematicsMathematical analysisStatisticsThermodynamics

Abstract

fetched live from OpenAlex

Abstract By digitally imaging colonies with more than a hundred cells, the distributions of cell size and shape are determined for four examples of 2-Ga microfossils: bacillus-shaped Eosynechococcus moorei and three dyads or diplococci ( Sphaerophycus parvum and two forms of Eoentophysalis belcherensis ). By assuming that each colony obeys steady-state growth, the measured distributions can be inverted to infer the time evolution of the individual cell shape. The time evolution can also be predicted analytically from rate-based models of cell growth, permitting the data to distinguish among different postulates for the physical principles governing growth. The cell cycles are found to be best described by the exponential growth of cell volume, although linear volume growth is not ruled out. However, the measured dyad cycles are inconsistent with several growth models based on surface area or the behavior of the septum at the division plane. Where they have been measured, modern bacilli obey exponential growth whereas eukaryotics obey linear growth, which implies that these 2-Ga microfossils are likely prokaryotic.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2007
Admission routes1
Has abstractyes

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