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Record W3133931223 · doi:10.1594/pangaea.913907

Metadata and NCBI-Accession numbers of 16S data for seawater, sediment, biofilm, and Vazella pourtalesii sponges from the Scotian Shelf (Canada) in summer 2016 and 2017

2020· dataset· en· W3133931223 on OpenAlexaboutno aff
Kathrin Busch, Lindsay Beazley, Ellen Kenchington, Frederick G. Whoriskey, Beate M. Slaby, Ute Hentschel

Bibliographic record

VenuePublishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research) · 2020
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
FundersHorizon 2020
KeywordsMetadataSedimentAccessionOceanographySeawaterAccession number (library science)FisheryGeographyWorld Wide WebBiologyGeologyBusinessComputer science

Abstract

fetched live from OpenAlex

Anthropogenic pressures on marine ecosystems and organisms are steadily increasing. Whether and to which extent these pressures influence marine biodiversity is only starting to unveil. Establishment of adequate conservation areas represents a challenging but crucial task in the conservation of genetic diversity and biological variability. Using 16S rRNA gene amplicon sequencing, we analysed the microbial community structure of 32 individuals of the habitat-forming glass sponge Vazella pourtalesii, as well as reference seawater, sediment, and biofilm samples. We assessed how two anthropogenic impacts, i.e. habitat destruction by trawling and artificial substrate provision (moorings made of composite plastic), correspond with in situ V. pourtalesii microbiome variability. In addition, we evaluated the role of two bottom fishery closures in preserving sponge-associated microbial diversity on the Scotian Shelf, Canada.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.004
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 head0.099
GPT teacher head0.316
Teacher spread0.218 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePublishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)Same topicIdentification and Quantification in FoodFrench-language works237,207