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Record W4362734976 · doi:10.3897/bdj.11.e101579

A new semi-slug of the genus Microparmarion from Brunei, discovered, described and DNA-barcoded on citizen-science 'taxon expeditions' (Gastropoda, Stylommatophora, Ariophantidae)

2023· article· en· W4362734976 on OpenAlexaff
Menno Schilthuizen, Simon Berenyi, Nurilya Ezzwan, Nur Izzah Hamdani, Harrison Wu, Luca De Antoni, Leonardo Vincenzi, Werner de Gier, Anthonie van Peursen, Iva Njunjić, Massimo Delledonne, Ferry Slik, T. Ulmar Grafe, Daniele Cicuzza

Bibliographic record

VenueBiodiversity Data Journal · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMollusks and Parasites Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General Hospital
FundersUniversiti Brunei Darussalam
KeywordsDNA barcodingGastropodaTaxonBiologyGenusSlugEcologyPhylogenetic treeNational parkZoology

Abstract

fetched live from OpenAlex

During citizen-science expeditions to the Ulu Temburong National Park, Brunei, several individuals were collected of a semi-slug species of the genus Microparmarion that, based on morphology and in-the-field DNA-barcoding, was found to be an undescribed species. In this paper, we describe Microparmarion sallehi Wu, Ezzwan & Hamdani, n. sp., after field centre supervisor Md Salleh Abdullah Bat. We provide details on the external and internal reproductive morphology, the shell and the ecology of the type locality, as well as a diagnosis comparing it with related species. DNA barcodes were generated for five individuals and used for a phylogenetic reconstruction. Microparmarion sallehi sp. n. and M. exquadratus Schilthuizen et al., 2019 so far are the only Bornean species of the genus that live in lowland forest; other species are found in montane forests.

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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.233
Teacher spread0.186 · 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
Published2023
Admission routes1
Has abstractyes

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