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Record W2168967400 · doi:10.1242/jeb.110817

Elements of a ‘nervous system’ in sponges

2015· review· en· W2168967400 on OpenAlexaff
Sally P. Leys

Bibliographic record

VenueJournal of Experimental Biology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMarine Sponges and Natural Products
Canadian institutionsUniversity of Alberta
FundersUniversity of OxfordNational Science Foundation
KeywordsSpongeBiologyContext (archaeology)Evolutionary biologyTranscriptomeNeuroscienceSensory systemComputational biologyGeneGeneticsPaleontology

Abstract

fetched live from OpenAlex

Genomic and transcriptomic analyses show that sponges possess a large repertoire of genes associated with neuronal processes in other animals, but what is the evidence these are used in a coordination or sensory context in sponges? The very different phylogenetic hypotheses under discussion today suggest very different scenarios for the evolution of tissues and coordination systems in early animals. The sponge genomic 'toolkit' either reflects a simple, pre-neural system used to protect the sponge filter or represents the remnants of a more complex signalling system and sponges have lost cell types, tissues and regionalization to suit their current suspension-feeding habit. Comparative transcriptome data can be informative but need to be assessed in the context of knowledge of sponge tissue structure and physiology. Here, I examine the elements of the sponge neural toolkit including sensory cells, conduction pathways, signalling molecules and the ionic basis of signalling. The elements described do not fit the scheme of a loss of sophistication, but seem rather to reflect an early specialization for suspension feeding, which fits with the presumed ecological framework in which the first animals evolved.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.369
Teacher spread0.333 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations147
Published2015
Admission routes1
Has abstractyes

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