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Record W2940529905 · doi:10.1007/s13280-019-01185-y

Circumpolar terrestrial arthropod monitoring: A review of ongoing activities, opportunities and challenges, with a focus on spiders

2019· review· en· W2940529905 on OpenAlexaff
Mark A. Gillespie, Matthías Alfreðsson, Isabel C. Barrio, Joe Bowden, Peter Convey, Stephen J. Coulson, Lauren E. Culler, Martin T. Dahl, Kathryn M. Daly, Seppo Koponen, Sarah Loboda, Yuri M. Marusik, Jonas Sandström, Derek S. Sikes, Jozef Slowik, Toke T. Høye

Bibliographic record

VenueAMBIO · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsMcGill University
FundersNatural Environment Research CouncilSight Research UK
KeywordsCircumpolar starArthropodBiodiversityArcticBaseline (sea)TaxonEcologyGeographyEnvironmental resource managementSpiderBiologyEnvironmental scienceFisheryOceanography

Abstract

fetched live from OpenAlex

The terrestrial chapter of the Circumpolar Biodiversity Monitoring Programme (CBMP) has the potential to bring international multi-taxon, long-term monitoring together, but detailed fundamental species information for Arctic arthropods lags far behind that for vertebrates and plants. In this paper, we demonstrate this major challenge to the CBMP by focussing on spiders (Order: Araneae) as an example group. We collate available circumpolar data on the distribution of spiders and highlight the current monitoring opportunities and identify the key knowledge gaps to address before monitoring can become efficient. We found spider data to be more complete than data for other taxa, but still variable in quality and availability between Arctic regions, highlighting the need for greater international co-operation for baseline studies and data sharing. There is also a dearth of long-term datasets for spiders and other arthropod groups from which to assess status and trends of biodiversity. Therefore, baseline studies should be conducted at all monitoring stations and we make recommendations for the development of the CBMP in relation to terrestrial arthropods more generally.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.157
GPT teacher head0.287
Teacher spread0.130 · 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 designOther design
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

Citations24
Published2019
Admission routes1
Has abstractyes

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