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Record W2911492097 · doi:10.3390/d11020022

Boreal Sand Hills are Areas of High Diversity for Boreal Ants (Hymenoptera: Formicidae)

2019· article· en· W2911492097 on OpenAlexaffabout
James R. N. Glasier, Scott E. Nielsen, John Acorn, Jaime Pinzón

Bibliographic record

VenueDiversity · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversity of Alberta
Fundersnot available
KeywordsSpecies richnessEcologyBiodiversityBorealEcosystemSpecies diversityTaigaInvertebrateEnvironmental scienceGeographyBiology

Abstract

fetched live from OpenAlex

The boreal forest in Canada comprises a wide variety of ecosystems, including stabilized (overgrown) sand dunes, often referred to as sand hills. Globally, sandy soils are known for supporting a high diversity of invertebrates, including ants, but little is known for boreal systems. We used pitfall trap sampling in sand hill, aspen parkland and peatland ecosystems to compare their ant diversity and test the prediction that areas with sandy soils have higher invertebrate diversity compared to more mesic/organic soils. Overall, sand hills had ~45% more ant species compared to other sampled ecosystems from Alberta. Similar to other studies, local canopy cover within sand hills was found to be inversely related to ant species richness and diversity. Although sand hills are rare across the region, they are high biodiversity areas for ants, with the sand hills of north-central Alberta having higher species richness of ants compared to other studied areas in Canada.

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 score0.596
Threshold uncertainty score0.813

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 studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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