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Record W3000656765 · doi:10.1007/s13280-019-01303-w

Arctic terrestrial biodiversity status and trends: A synopsis of science supporting the CBMP State of Arctic Terrestrial Biodiversity Report

2020· review· en· W3000656765 on OpenAlexaff
Jason J. Taylor, James P. Lawler, Mora Aronsson, Tom Barry, Anne D. Bjorkman, Tom Christensen, Stephen J. Coulson, Christine Cuyler, Dorothée Ehrich, Knud Falk, Alastair Franke, Eva Fuglei, Mark A. Gillespie, Starri Heiðmarsson, Toke T. Høye, Liza K. Jenkins, Virve Ravolainen, Paul A. Smith, Paweł Wąsowicz, Niels Martin Schmidt

Bibliographic record

VenueAMBIO · 2020
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsEnvironment and Climate Change CanadaAurora College
FundersMiljøstyrelsenNordisk Ministerråd
KeywordsBiodiversityCircumpolar starArcticGeographyThe arcticEnvironmental resource managementEcosystemEcologyTerrestrial ecosystemEnvironmental scienceOceanographyBiology

Abstract

fetched live from OpenAlex

This review provides a synopsis of the main findings of individual papers in the special issue Terrestrial Biodiversity in a Rapidly Changing Arctic. The special issue was developed to inform the State of the Arctic Terrestrial Biodiversity Report developed by the Circumpolar Biodiversity Monitoring Program (CBMP) of the Conservation of Arctic Flora and Fauna (CAFF), Arctic Council working group. Salient points about the status and trends of Arctic biodiversity and biodiversity monitoring are organized by taxonomic groups: (1) vegetation, (2) invertebrates, (3) mammals, and (4) birds. This is followed by a discussion about commonalities across the collection of papers, for example, that heterogeneity was a predominant pattern of change particularly when assessing global trends for Arctic terrestrial biodiversity. Finally, the need for a comprehensive, integrated, ecosystem-based monitoring program, coupled with targeted research projects deciphering causal patterns, is discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Review
Teacher disagreement score0.857
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.296
Teacher spread0.216 · 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.

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

Citations35
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAMBIOSame topicClimate change and permafrostFrench-language works237,207