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Record W7010759782

Investigating the species diversity of Planorbella and Helisoma (Gastropoda: Hygrophila: Planorbidae) in Manitoba

2021· dissertation· en· W7010759782 on OpenAlexafffundabout

Bibliographic record

VenueMspace (University of Manitoba) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpecies complexCladePhylogenetic treeNuclear genePhylogeneticsMitochondrial DNASpecies diversityGenetic diversity
DOInot available

Abstract

fetched live from OpenAlex

Species-level resolution is lacking for freshwater gastropods in Family Planorbidae, with factors like cryptic species, phenotypic variation, and potential misidentifications confounding estimates of biodiversity. To characterize the diversity of five nominal planorbid species (four Planorbella and one Helisoma), I compared descriptive morphology-based identifications to phylogenetic (COI gene) and geometric morphometric analyses of shell shape. I recovered five genetically distinct clades though two clades may contain cryptic species. Shell shape analysis confirmed morphological overlap between some species and confirmed that shell-based identifications can be problematic for species-level identification. For each of the five clades, I sequenced the complete mitogenomes and nuclear rRNA repeat regions. Comparisons among the mitochondrial (COI gene and mitogenome) and nuclear gene phylogenies (concatenated 18S/28S, ITS1-5.8S-ITSII, rRNA repeat region) resulted in some incongruence suggesting past hybridization between two species. These findings show that an integrative approach to species delimitation is essential for understanding the biology of freshwater gastropods.

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)0.000
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 score0.853
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.188
Teacher spread0.171 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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