MétaCan
Menu
Back to cohort
Record W2343823116 · doi:10.14288/1.0089581

Molecular approaches to systematic problems in parasitic nematodes : ribosomol DNA variation within cystidicola spp. (Nematoda: Habronematoidea) and the superfamily dracunculoidea

2009· article· en· W2343823116 on OpenAlexaboutno aff
Allyson E. Miscampbell

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsSUPERFAMILYBiologyEvolutionary biologyHelminthsVariation (astronomy)ZoologyGeneticsComputational biologyGene

Abstract

fetched live from OpenAlex

Morphological characters are traditionally used in nematode systematics, however, morphological convergence and marginal differences between close relatives can obscure species diversity and confound taxonomic studies. This thesis applies molecular approaches to systematic problems in two groups of parasitic nematodes where morphological data is ambiguous. Ribosomal DNA (rDNA) variable regions such as the first and second internal transcribed spacers (ITS-1 and ITS-2), and the D3 expansion loop of the large subunit have consistently distinguished nematode species and provided a limited basis for phylogenetic inference between close relatives. I assess rDNA variation within Cystidicola spp. (Nematoda; Habronematoidea) and the superfamily Dracunculoidea to examine species diversity in both groups, and phylogenetic relationships in the Dracunculoidea. Phenotypic variation in Cystidicola spp. suggests unresolved variation within the genus. Distinct life histories, host ranges, reproductive strategies, and adult and egg morphologies define the two recognized Cystidicola spp. Variable host specificity and egg morphology in Cystidicola farionis is difficult to interpret and could reflect genetic species-level variation. I sequenced four rDNA regions (ITS-1, ITS-2, 5.8S, D3) from Cystidicola spp. isolates from a total of seven host species and nine locations in Ontario (ONT), British Columbia (BC) and Finland (FIN). The ITS-1, 5.8S, and D3 regions displayed no inter or intraspecific variation. Two ITS-2 types were identified which differed at four nucleotide positions: the ITS-2 from C. farionis (BC) and C. stigmatura was identical and 365bp long; the ITS-2 from ONT and FIN C. farionis was identical and 368bp long. No relationship between egg morphology and genetic variation was apparent. ITS-2 differences between morphologically distinct C. farionis (ONT and FIN) and C. stigmatura were expected but comparison of this region among C. farionis isolates produced a surprising result. The ITS-2 distinguishes C. farionis (BC) from C. farionis (ONT and FIN) and suggests a closer relationship between C. farionis (BC) and C. stigmatura. Morphological resemblance among close relatives and a lack of phylogenetically informative characters in the superfamily Dracunculoidea reiterates this need for more precise taxonomic markers. I examined the D3 and ITS-2 regions from a total of nine dracunculoid species to distinguish cryptic species (e.g. Philonema spp.), place unidentified nematodes within the current classification system, and infer phylogenetic relationships within dracunculoid families (e.g. the Philometridae and Guyanemidae). I sequenced the D3 of two dracunculoid species, Philometroides huronensis and an unidentified nematode from Eopsetta exilis, adding these to an existing D3 data set of seven dracunculoids and sequenced the ITS-2 from all nine species. These regions varied in their ability to distinguish close relatives. The D3 region distinguishes Philonema agubernaculum and P. oncorhynchi but not Cystidicola spp. whereas the ITS-2 is identical in the former taxa and distinct in the latter. Both ITS-2 and D3 data supported previous suggestions that the family Philometridae may be artificial, and that members of the Guyanemidae are affiliated with some philometrids (e.g. Philonema spp.).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.158
Teacher spread0.140 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicNematode management and characterization studiesFrench-language works237,207