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Record W2887824947 · doi:10.1002/bies.201700237

Understanding Animal Evolution: The Added Value of Sponge Transcriptomics and Genomics

2018· review· en· W2887824947 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioEssays · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMarine Sponges and Natural Products
Canadian institutionsUniversity of Alberta
FundersAgence Nationale de la RechercheUniversity of AlbertaDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaLudwig-Maximilians-Universität MünchenUniversité Catholique de LouvainEuropean CommissionUniversity of Denver
KeywordsSpongeBiologyTranscriptomeEvolutionary biologyGenomicsGenomeComputational biologyZoologyGeneticsGene

Abstract

fetched live from OpenAlex

Sponges are important but often-neglected organisms. The absence of classical animal traits (nerves, digestive tract, and muscles) makes sponges challenging for non-specialists to work with and has delayed getting high quality genomic data compared to other invertebrates. Yet analyses of sponge genomes and transcriptomes currently available have radically changed our understanding of animal evolution. Sponges are of prime evolutionary importance as one of the best candidates to form the sister group of all other animals, and genomic data are essential to understand the mechanisms that control animal evolution and diversity. Here we review the most significant outcomes of current genomic and transcriptomic analyses of sponges, and discuss limitations and future directions of sponge transcriptomic and genomic studies.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.094
GPT teacher head0.300
Teacher spread0.206 · 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