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Record W4391971044 · doi:10.1007/s00227-024-04400-x

The microbial community of coral reefs: biofilm composition on artificial substrates under different environmental conditions

2024· article· en· W4391971044 on OpenAlexfundno aff
Roy Yanovski, Hana Barak, Itzchak Brickner, Ariel Kushmaro, Avigdor Abelson

Bibliographic record

VenueMarine Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersTel Aviv UniversityDalhousie University
KeywordsBiologyCoralBiofilmCoral reefArtificial reefEcologyReefComposition (language)Microbial population biologyBiofoulingOceanographyBacteria

Abstract

fetched live from OpenAlex

Abstract Artificial reefs are used as tools for the restoration of degrading coral reefs by providing new settlement substrates. The initial recruitment process consists of the formation of microbial biofilms shortly after deployment. The aim of the present study is to compare biofilm composition and development on artificial substrates at two different coral-reef sites. These locations differ in their environmental conditions, including their level of anthropogenic impact. Substrate and seawater samples were collected four times during the first 6 months after deployment, using a new methodology termed ‘tab-by-tab’. DNA was extracted, sequenced, and sorted for both prokaryotic (16s) and eukaryotic (18s) genes. No difference was found between the planktonic communities in the water, yet significant differences were observed in the biofilm communities on the structures at the two sites. Moreover, differences were recorded in coral recruitment rates, which are known to be affected by biofilm composition. Our findings suggest a potential role of environmental conditions in the early biofilm stages (first few months), which in turn may impact the succession and development of coral-reef communities and the success of artificial reefs as restoration tools.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.825

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)0.0010.000

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 head0.020
GPT teacher head0.243
Teacher spread0.224 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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