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Record W2005645024 · doi:10.1111/jeu.12027

Molecular Characterization of Parabasalian Symbionts <i><scp>C</scp>oronympha clevelandii</i> and <i><scp>T</scp>richonympha subquasilla</i> from the <scp>H</scp>awaiian Lowland Tree Termite <i><scp>I</scp>ncisitermes immigrans</i>

2013· article· en· W2005645024 on OpenAlexafffund
E. James, Fabien Burki, James Harper, Rudolf H. Scheffrahn, Patrick J. Keeling

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsDouglas CollegeUniversity of British Columbia
FundersTula FoundationUniversity of FloridaNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsBiologyDNA barcodingPhylogenetic treeHost (biology)ZoologyGenusEvolutionary biologyEcologyGeneGenetics

Abstract

fetched live from OpenAlex

An important and undervalued challenge in characterizing symbiotic protists is the accurate identification of their host species. Here, we use DNA barcoding to resolve one confusing case involving parabasalian symbionts in the hindgut of the Hawaiian lowland tree termite, Incisitermes immigrans, which is host to several parabasalians, including the type species for the genus Coronympha, C. clevelandii. We collected I. immigrans from its type locality (Hawaii), confirmed its identity by DNA barcoding, and characterized the phylogenetic position of two symbionts, C. clevelandii and Trichonympha subquasilla. These data show that previous molecular surveys of "I. immigrans" are, in fact, mainly derived from the Caribbean termite I. schwarzi, and perhaps also another related species. These results emphasize the need for host barcoding, clarify the relationship between morphologically distinct Coronympha species, and also suggest some interesting distribution patterns of nonendemic termite species and their symbionts.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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 designBench or experimental
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

Citations8
Published2013
Admission routes2
Has abstractyes

Explore more

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