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Record W2793886733 · doi:10.2988/17-00005

Population genetic structure of the intertidal kinorhynch<i>Echinoderes marthae</i>(Kinorhyncha; Cyclorhagida; Echinoderidae) across the São Sebastião Channel, Brazil

2018· article· en· W2793886733 on OpenAlexaff
Phillip V. Randsø, Maikon Di Domênico, María Herranz, Eline D. Lorenzen, Martin V. Sørensen

Bibliographic record

VenueProceedings of the Biological Society of Washington · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of British Columbia
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsIntertidal zoneBiological dispersalGeographyHabitatPopulationBayChannel (broadcasting)Gene flowEcologyOceanographyGeologyBiologyGenetic variation

Abstract

fetched live from OpenAlex

Barriers to gene flow in marine environments vary between species and are highly dependent on dispersal ability and habitat discontinuity. Intertidal sand and mud flats are discontinuous areas, separated by other habitat types along a coastline or by subtidal zones. The São Sebastião Channel in eastern Brazil, which is situated between Rio de Janeiro and São Paulo, harbors two intertidal mud flats where populations of the kinorhynch Echinoderes marthae are found, one on the mainland (Araçá Bay) and one on São Sebastião Island (Ilhabela). Here, we investigated the genetic structure of two E. marthae populations across the São Sebastião Channel, in order to contribute to the ongoing debate on biogeography of meiobenthic animals. Based on 628 bp of the mitochondrial CO1 gene, we find that E. marthae shows low levels of structure in the São Sebastião Channel (FST = 0.165), and find evidence of recent demographic expansion across populations.

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 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 score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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.

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

Citations11
Published2018
Admission routes1
Has abstractyes

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