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Record W4213341797 · doi:10.1128/msystems.00991-21

Microbial Functional Diversity Correlates with Species Diversity along a Temperature Gradient

2022· article· en· W4213341797 on OpenAlexafffund
Ilona A. Ruhl, Andriy Sheremet, Angela V. Smirnova, Christine Sharp, Stephen E. Grasby, Marc Strous, Peter F. Dunfield

Bibliographic record

VenuemSystems · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsGeological Survey of CanadaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsDiversity (politics)EcologyBiologyFunctional diversityEvolutionary biologySociologyAnthropology

Abstract

fetched live from OpenAlex

Only recently have microbial ecologists begun to assess quantitatively how microbial species diversity correlates with environmental factors like pH, temperature, and salinity. However, still, very few studies have examined how the number of distinct biochemical functions of microbial communities, termed functional diversity, varies with the same environmental factors. Our study examined 18 microbial communities sampled across a wide temperature gradient and found that increasing temperature reduced both species and functional diversity, but in different ways. Initially, functional diversity increased sharply with increasing species diversity but eventually plateaued, following a power function. This pattern has been previously predicted in theoretical models, but our study validates this predicted power function with field metagenomic data. This study also presents a unique overview of the distribution of metabolic functions along a temperature gradient, demonstrating that many functions have temperature "ceilings" above which they are no longer found.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.012
GPT teacher head0.156
Teacher spread0.145 · 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

Citations52
Published2022
Admission routes2
Has abstractyes

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