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Record W2918135199 · doi:10.1038/s41598-019-39274-3

Genome analysis of the rice coral Montipora capitata

2019· article· en· W2918135199 on OpenAlexfundno aff
Alexander Shumaker, Hollie M. Putnam, Huan Qiu, Dana C. Price, Ehud Zelzion, Arye Harel, Nicole Wagner, Ruth D. Gates, Hwan Su Yoon, Debashish Bhattacharya

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersOffice of Experimental Program to Stimulate Competitive ResearchNational Science FoundationKorea Institute of Marine Science and Technology promotionMinistry of Oceans and FisheriesPaul G. Allen Family FoundationConcordia University
KeywordsBiologyCoralGenomeCoral reefPopulationDinoflagellateMetagenomicsGeneEvolutionary biologyEcologyGenetics

Abstract

fetched live from OpenAlex

Corals comprise a biomineralizing cnidarian, dinoflagellate algal symbionts, and associated microbiome of prokaryotes and viruses. Ongoing efforts to conserve coral reefs by identifying the major stress response pathways and thereby laying the foundation to select resistant genotypes rely on a robust genomic foundation. Here we generated and analyzed a high quality long-read based ~886 Mbp nuclear genome assembly and transcriptome data from the dominant rice coral, Montipora capitata from Hawai'i. Our work provides insights into the architecture of coral genomes and shows how they differ in size and gene inventory, putatively due to population size variation. We describe a recent example of foreign gene acquisition via a bacterial gene transfer agent and illustrate the major pathways of stress response that can be used to predict regulatory components of the transcriptional networks in M. capitata. These genomic resources provide insights into the adaptive potential of these sessile, long-lived species in both natural and human influenced environments and facilitate functional and population genomic studies aimed at Hawaiian reef restoration and conservation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 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

Citations87
Published2019
Admission routes1
Has abstractyes

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