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Record W6962489063 · doi:10.17632/fx3vd2tgcf.2

Data for: Putative past, present, and future spatial distributions of deep-sea coral and sponge microbiomes revealed by predictive models

2025· dataset· en· W6962489063 on OpenAlexaff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsCoralAbundance (ecology)Host (biology)SalinitySpongeMicrobiomeMetadataReplicate

Abstract

fetched live from OpenAlex

This dataset includes output rasters of the spatio-temporal model presented in Busch et al. 2024 (Data folder 1) giving cumulative microbial richnesses in deep-sea sponges and corals for the present, and two future scenarios. We also provide point data on the environmental variables used in our predictions (Data file 2): Depth, Slope, Bottom Stress (“BtmStress”), Bottom Current Speed (“BtmCur”), Mixed Layer Depth (“MLD”), Bottom Salinity (“BtmSal”), Sea Surface Salinity (“SSS”), Sea Surface Temperature (“SST”), and Bottom Temperature (“BtmTmp”) averaged for each of the seven time periods (extracted from the Simple Ocean Data Assimilation, SODA, for the past periods 1871-1900, 1901-1930, 1931-1960, 1961-1989; extracted from the BNAM Ocean Model for the present period 1990-2015 and for the future periods 2046-2065 and 2066-2085 under a RCP8.5 scenario). This dataset also contains raw occurrence data compiled from different indicated sources (Data file 3), and presence/absence data (true absences as well as pseudo absences) gridded to be used as model input (Data file 4) of the analysed host species Weberella bursa, Stryphnus fortis, Lophelia pertusa, Desmophyllum dianthus, and Vazella pourtalesii. Another data file (Data file 5) contains an overview of the ENA (European Nucleotide Archive) accession numbers, the original literature sources, and some basic ecological metadata of the used microbial data (16S amplicon data) drawn from the host species. After assigning the microbial abundance status (HMA – high microbial abundance, and LMA – low microbial abundance) to ten key sponge species in the Flemish Cap area area (Asconema foliatum, Geodia barretti, Geodia macandrewii, Geodia parva-phlegraei, Mycale lingua, Stelletta normani, Stryphnus fortis, Stylocordyla borealis, Tentorium semisuberites, and Weberella bursa) we correlated our spatial predictions of status occurrence with predictions of overall ecosystem function, i.e. here nutrient cycling and habitat provision (derived from Murillo et al. 2020, Diversity and Distributions). For our study the original rasters (created by Murillo et al. 2020) were resampled to a 0.088 cell size using Bilinear interpolation as the resampling technique in ArcGIS Pro. Data 6 contains the resampled predictions of overall ecosystem function, as well as our predicted occurrences of the HMA and LMA status, and the respective summed biomasses. In our article (above) we show a biomass network, integrating the generated information on HMA and LMA sponge biomasses with biomass measurements of other sessile filter feeding invertebrates, which occur in high abundances at the Flemish Cap (data taken from Murillo et al. 2020, Diversity and Distributions). Data file 7 contains the underlying biomass data used for the network, covering 116 different species, which are classified according to size (small, medium, medium large, large), functional (passive “PFF”, and active “AFF” filter feeders) and taxonomic (8 phyla) groups.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.088
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0880.051

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 head0.040
GPT teacher head0.322
Teacher spread0.281 · 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 designSimulation or modeling
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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