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Record W4365814740 · doi:10.1007/s00300-023-03131-x

Metabarcoding inventory of an arctic tundra soil ecosystem reveals highly heterogeneous communities at a small scale

2023· article· en· W4365814740 on OpenAlexafffundabout
Jesse Jorna, Bryan Vandenbrink, Ian D. Hogg, Diana H. Wall, Byron J. Adams

Bibliographic record

VenuePolar Biology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Guelph
FundersPolar Knowledge Canada
KeywordsTundraEcologyBiologyArcticSpecies richnessBiodiversityEcosystemFaunaBayHabitatArthropodInvertebrateGeography

Abstract

fetched live from OpenAlex

Abstract Biodiversity surveys of Arctic soil ecosystems are limited. Here, we provide a sequence-based inventory of soil fauna from an Arctic tundra ecosystem near the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. Invertebrate communities were extracted at a vegetated and non-vegetated site in three replicates and inventoried using 18S metabarcode sequencing. A total of 361 amplicon sequence variants (ASV) were identified and assigned to the closest matching taxonomic orders, most of which belonged to the Nematoda and Arthropoda. Vegetated soils showed no significantly higher ASV richness relative to non-vegetated soils although they contained a significantly higher diversity of arthropod taxa including insects, mites, and springtails. Most taxa were found only at a single location and even samples from the same site displayed distinct communities, suggesting that belowground species richness in Arctic tundra habitats is highly heterogeneous. Preserving soil biodiversity in a changing Arctic is essential for Inuit communities who rely on intact tundra ecosystems for their health and wellbeing.

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 score0.033
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.035
GPT teacher head0.231
Teacher spread0.196 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

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