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Record W2922020938 · doi:10.1007/s13280-019-01162-5

Status and trends of terrestrial arthropod abundance and diversity in the North Atlantic region of the Arctic

2019· article· en· W2922020938 on OpenAlexaff
Mark A. Gillespie, Matthías Alfreðsson, Isabel C. Barrio, Joseph J. Bowden, Peter Convey, Lauren E. Culler, Stephen J. Coulson, Paul Henning Krogh, Amanda M. Koltz, Seppo Koponen, Sarah Loboda, Yuri M. Marusik, Jonas Sandström, Derek S. Sikes, Toke T. Høye

Bibliographic record

VenueAMBIO · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsMcGill University
FundersNatural Environment Research CouncilSight Research UK
KeywordsCircumpolar starAbundance (ecology)ArcticBiodiversityMirroringArthropodEcologyGeographyEcosystemBaseline (sea)Diversity (politics)The arcticBiologyOceanographyFishery

Abstract

fetched live from OpenAlex

The Circumpolar Biodiversity Monitoring Programme (CBMP) provides an opportunity to improve our knowledge of Arctic arthropod diversity, but initial baseline studies are required to summarise the status and trends of planned target groups of species known as Focal Ecosystem Components (FECs). We begin this process by collating available data for a relatively well-studied region in the Arctic, the North Atlantic region, summarising the diversity of key terrestrial arthropod FECs, and compiling trends for some representative species. We found the FEC classification system to be challenging to implement, but identified some key groups to target in the initial phases of the programme. Long-term data are scarce and exhibit high levels of spatial and temporal variability. Nevertheless, we found that a number of species and groups are in decline, mirroring patterns in other regions of the world. We emphasise that terrestrial arthropods require higher priority within future Arctic monitoring programmes.

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)0.001
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 score0.143
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

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

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 head0.199
Teacher spread0.189 · 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

Citations63
Published2019
Admission routes1
Has abstractyes

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Same venueAMBIOSame topicFire effects on ecosystemsFrench-language works237,207