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Record W4414201389 · doi:10.18061/bssb.5811

A Common Garden of <em>Halichondria </em>Sponges: Taxonomic Revision of Northeast Pacific Halichondriidae Reveals Many Cryptic Introduced Species

2025· article· en· W4414201389 on OpenAlexaff

Bibliographic record

VenueBulletin of the Society of Systematic Biologists · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntellectual Property Law
Canadian institutionsHuntsman Marine Science Centre
Fundersnot available
KeywordsSympatric speciationReproductive isolationSpecies complexCladeHabitatTaxonomy (biology)BiogeographyDiversification (marketing strategy)

Abstract

fetched live from OpenAlex

Sponges (phylum Porifera) possess biochemical, cellular, and physiological traits with valuable biotechnical applications. However, our ability to harness these natural innovations is limited by a classification system that does not fully reflect their evolutionary history. In this study, we uncover numerous cryptic species within the genus Halichondria that are morphologically indistinguishable from the well-known Ha. panicea. Many of these species have habitat preferences and geographic distributions that strongly suggest they have been dispersed by human activity. Most of these species are broadly sympatric with their closest relatives, and these overlapping distributions allow us to use patterns of DNA variation to infer reproductive isolation between clades in nature. With reproductively isolated species thus delineated, we can use DNA states as taxonomic characters to formally describe them. Though much remains to be learned about these newly discovered species, the natural “common gardens” of these sponges in California, New York, and other locations provide opportunities to test hypotheses about their diversification in future work.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.261
Teacher spread0.230 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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