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Record W4283396957 · doi:10.3897/zookeys.1107.82976

The Coleoptera of the Province of Prince Edward Island, Canada: 295 new records from Lindgren funnel traps and a checklist to species

2022· article· en· W4283396957 on OpenAlexafffundabout
Reginald P. Webster, Cory Hughes, Jon Sweeney

Bibliographic record

VenueZooKeys · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversity of New Brunswick
FundersNatural Resources Canada
KeywordsFaunaDeciduousGeographyEcologySpecies richnessUnderstoryLonghorn beetleNearctic ecozoneForestryBiologyArchaeologyCanopyTaxonomy (biology)

Abstract

fetched live from OpenAlex

The Coleoptera fauna of the province of Prince Edward Island has long been one of the most poorly known jurisdictions in Canada, with fewer than half the number of species recorded in the neighbouring provinces of New Brunswick and Nova Scotia. If much of the difference in species richness was due to less intensive sampling of the province compared to other parts of Atlantic Canada it was predicted that surveys with semiochemical-baited traps would detect many previously undetected species. Lindgren funnel traps were baited with longhorn beetle pheromones and host volatiles and placed in the canopy and understory of coniferous and deciduous trees at the Valleyfield, New Harmony, Auburn, and Brookvale Demonstration Woodlots during the summers of 2018 and 2019. Two hundred and ninety-five species of Coleoptera are newly recorded from Prince Edward Island from 53 families. One of these, the Palaearctic Pityophagus ferrugineus (Linnaeus, 1760) is reported for the first time from North America and Canada. The families Lycidae, Derodontidae, Lymexylidae, Sphindidae, Cucujidae, Ripiphoridae, Salpingidae, and Nemonychidae are newly recorded for the province. A checklist of the Coleoptera of Prince Edward Island is provided.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.176
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 teacher head, not a consensus.

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

Citations6
Published2022
Admission routes3
Has abstractyes

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