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Record W2913807100 · doi:10.3897/zookeys.819.31947

The diversity of terrestrial arthropods in Canada

2019· review· en· W2913807100 on OpenAlexaffabout
David W. Langor

Bibliographic record

VenueZooKeys · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicCollembola Taxonomy and Ecology Studies
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsFaunaHolarcticHymenopteraBiologyEcologyTaxonomy (biology)Species richnessBiodiversityDNA barcodingZoologyGenus

Abstract

fetched live from OpenAlex

Special Issue of ZooKeys and data provided herein about Zygentoma, more than 44,100 described species of terrestrial arthropods (Arachnida, Myriapoda, Insecta, Entognatha) are now known from Canada. This represents more than a 34% increase in the number of described species reported 40 years ago (Danks 1979a). The most speciose groups are Diptera (9620 spp.), Hymenoptera (8757), and Coleoptera (8302). Less than 5% of the fauna has a natural Holarctic distribution and an additional 5.1% are non-native species. A conservatively estimated 27,000-42,600 additional species are expected to be eventually discovered in Canada, meaning that the total national species richness is ca. 71,100-86,700 and that currently 51-62% of the fauna is known. Of the most diverse groups, those that are least known, in terms of percent of the Canadian fauna that is documented, are Acari (31%), Thysanoptera (37%), Hymenoptera (46%), and Diptera (32-65%). All groups but Pauropoda have DNA barcodes based on Canadian material. More than 75,600 Barcode Index Numbers have been assigned to Canadian terrestrial arthropods, 63.5% of which are Diptera and Hymenoptera. Much work remains before the Canadian fauna is fully documented, and this will require decades to achieve. In particular, greater and more strategic investment in surveys and taxonomy (including DNA barcoding) is needed to adequately document the fauna.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.077
GPT teacher head0.245
Teacher spread0.168 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations44
Published2019
Admission routes2
Has abstractyes

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