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Record W4243760648 · doi:10.3398/064.079.0101

Eight New Provincial Species Records of Mayflies (Ephemeroptera) from One Arctic Watershed River in British Columbia

2019· article· en· W4243760648 on OpenAlexaffabout
Dezene P.W. Huber, Claire M. Shrimpton, Daniel J. Erasmus

Bibliographic record

VenueWestern North American Naturalist · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsBaetidaeMayflyEcologyBiologyGeographyDNA barcodingArcticGenusNymphInvertebrate

Abstract

fetched live from OpenAlex

We repeatedly sampled 8 sites on the Crooked River in British Columbia's Arctic watershed for adult and nymph mayflies (Ephemeroptera) over the course of 2 years. Using taxonomic keys and DNA barcoding, we report 8 new species records for the province: 5 members of the family Baetidae (Acerpenna pygmaea, Baetis phoebus, Baetis vernus, Iswaeon anoka, and Procloeon pennulatum), 1 Heptageniidae (Leucrocuta hebe), 1 Leptohyphidae (Tricorythodes mosegus), and 1 Siphlonuridae (Siphlonurus alternatus). Three of these, Acerpenna, Iswaeon, and Leucrocuta, are also new genus records for the province. In total, we detected 40 species in 8 families as indicated by clustering into BINs (Barcode Index Numbers), by morphological keys, and by matches in the Barcode of Life Database. One of those species, Ameletus vernalis, is of conservation concern. Our analysis indicated that a number of other specimens may represent new species or genus records for British Columbia. In addition, this unique and anthropogenically impacted river may contain cryptic species of Baetis tricaudatus (Baetidae), Leptophlebia nebulosa (Leptophlebiidae), and Paraleptophlebia debilis (Leptophlebiidae).

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)0.001
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 score0.064
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.171
Teacher spread0.164 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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