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Pronounced seasonal dynamics of freshwater chitinase genes and chitin processing

2012· article· en· W1898095936 on OpenAlexaff
Sara Beier, Vani Mohit, Thijs J. G. Ettema, Örjan Östman, Lars J. Tranvik, Stefan Bertilsson

Bibliographic record

VenueEnvironmental Microbiology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicStudies on Chitinases and Chitosanases
Canadian institutionsUniversité Laval
FundersSvenska Forskningsrådet Formas
KeywordsBiologyChitinaseZooplanktonEcologyDaphniaPyrosequencingCommunity structureChitinSpecies richnessTemperate climatePhytoplanktonGeneNutrient

Abstract

fetched live from OpenAlex

Seasonal variation in activity of enzymes involved in polymer degradation, including chitinases, has been observed previously in freshwater environments. However, it is not known whether the seasonal dynamics are due to shifts in the activity of bacteria already present, or shifts in community structure towards emergence or disappearance of chitinolytic organisms. We traced seasonal shifts in the chitinase gene assemblage in a temperate lake and linked these communities to variation in chitinase activity. Chitinase genes from 20 samples collected over a full yearly cycle were characterized by pyrosequencing. Pronounced temporal shifts in composition of the chitinase gene pool (beta diversity) occurred along with distinct shifts in richness (alpha diversity) as well as chitin processing. Changes in the chitinase gene pool correlated mainly with temperature, abundance of crustacean zooplankton and phytoplankton blooms. Also changes in the physical structure of the lake, e.g. stratification and mixing were associated with changes in the chitinolytic community, while differences were minor between surface and suboxic hypolimnetic water. The lake characteristics influencing the chitinolytic community are all linked to changes in organic particles and we suggest that seasonal changes in particle quality and availability foster microbial communities adapted to efficiently degrade them.

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)0.000
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 score0.072
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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