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Record W2517394686 · doi:10.1038/srep32032

pH drop impacts differentially skin and gut microbiota of the Amazonian fish tambaqui (Colossoma macropomum)

2016· article· en· W2517394686 on OpenAlexafffund
François‐Étienne Sylvain, Bachar Cheaib, Martin Llewellyn, Tiago Gabriel Correia, Daniel Barros Fagundes, Adalberto Luís Val, Nicolas Derôme

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoInstituto Nacional de Pesquisas da AmazôniaFundação de Amparo à Pesquisa do Estado do AmazonasNatural Sciences and Engineering Research Council of CanadaMinistério da Ciência, Tecnologia e InovaçãoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTambaquiAmazonianBiologyGut floraFecesZoologyEcologyFish <Actinopterygii>Amazon rainforestFisheryBiochemistry

Abstract

fetched live from OpenAlex

Aquatic organisms are increasingly exposed to lowering of environmental pH due to anthropogenic pressure (e.g. acid rain, acid mine drainages). Such acute variations trigger imbalance of fish-associated microbiota, which in turn favour opportunistic diseases. We used the tambaqui (Colossoma macropomum), an Amazonian fish tolerant to significant pH variation in its natural environment, to assess the response of fish endogenous microbiota to acute short-term acid stress. We exposed 36 specimens of tambaquis to acidic water (pH 4.0) over 2 consecutive weeks and sampled cutaneous mucus, feces and water at 0, 7 &14 days. The 16S RNA hypervariable region V4 was sequenced on Illumina MiSeq. After two weeks of acidic exposure, fecal and skin microbiota taxonomic structures exhibited different patterns: skin microbiota was still exhibiting a significantly disturbed composition whereas fecal microbiota recovered a similar composition to control group, thus suggesting a stronger resilience capacity of the intestinal microbiota than cutaneous microbiota.

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 score0.172
Threshold uncertainty score0.399

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 studies0.0000.001
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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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

Citations182
Published2016
Admission routes2
Has abstractyes

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