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Record W2999107561 · doi:10.1007/s13280-019-01311-w

Developing a circumpolar programme for the monitoring of Arctic terrestrial biodiversity

2020· article· en· W2999107561 on OpenAlexafffund
Tom Christensen, Tom Barry, Jason J. Taylor, Marlene Doyle, Mora Aronsson, Jørund Braa, Casey T. Burns, Catherine Coon, Stephen J. Coulson, Christine Cuyler, Knud Falk, Starri Heiðmarsson, Pauliina Kulmala, James P. Lawler, Douglas MacNearney, Virve Ravolainen, Paul A. Smith, Mikhail Soloviev, Niels Martin Schmidt

Bibliographic record

VenueAMBIO · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAboriginal Affairs Northern Dev Canada
FundersLomonosov Moscow State UniversityAarhus UniversitetMiljø- og FødevareministerietMinistry of EnvironmentEnvironment and Climate Change CanadaNordisk Ministerråd
KeywordsCircumpolar starArcticBiodiversityEnvironmental resource managementEnvironmental monitoringThe arcticGeographyCitizen scienceEnvironmental planningEnvironmental scienceEcologyOceanography

Abstract

fetched live from OpenAlex

The Arctic is undergoing biological and environmental changes, and a coordinated effort to monitor is critical to detect these changes. The Circumpolar Biodiversity Monitoring Programme (CBMP) of the Arctic Council biodiversity working group, Conservation of Arctic Flora and Fauna (CAFF), has developed pan-Arctic biodiversity monitoring plans that aims to improve the ability to detect and report on long-term changes. Whilst introducing this special issue, this paper also presents the making of the terrestrial monitoring plan and discusses how the plan follows the steps required for an adaptive and ecosystem-based monitoring programme. In this article, we discuss how data on key findings can be used to inform circumpolar and global assessments, including the State of the Arctic Terrestrial Biodiversity Report, which will be the first terrestrial assessment made by the CBMP. Key findings, advice for future monitoring and lessons learned will be used in planning next steps of pan-Arctic coordinated monitoring.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.999

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 studies0.0020.000
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 head0.215
GPT teacher head0.390
Teacher spread0.175 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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Same venueAMBIOSame topicIndigenous Studies and EcologyFrench-language works237,207