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Record W2602244483 · doi:10.1371/journal.pbio.2001656

Antarctica and the strategic plan for biodiversity

2017· article· en· W2602244483 on OpenAlexaff
Steven L. Chown, Cassandra M. Brooks, Aleks Terauds, Céline Le Bohec, Céline van Klaveren-Impagliazzo, Jason D. Whittington, Stuart H. M. Butchart, Bernard W. T. Coetzee, Ben Collen, Peter Convey, Kevin J. Gaston, Neil Gilbert, Mike Gill, Robert Höft, Sam Johnston, Mahlon C. Kennicutt, Hannah Joy Kriesell, Yvon Le Maho, Heather J. Lynch, Maria Lourdes D. Palomares, Roser Puig-Marcó, Peter Stoett, Mélodie A. McGeoch

Bibliographic record

VenuePLoS Biology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsConcordia UniversityFisheries and Oceans CanadaUniversity of British ColumbiaGovernment of Canada
FundersNatural Environment Research CouncilCentre Scientifique de MonacoScientific Committee on Antarctic ResearchMonash UniversitySight Research UKGobierno del Principado de Asturias
KeywordsBiodiversityConvention on Biological DiversityScope (computer science)BiologyAction planStrategic planningPlan (archaeology)Environmental resource managementEnvironmental planningEcologyBusinessGeographyEconomics

Abstract

fetched live from OpenAlex

The Strategic Plan for Biodiversity, adopted under the auspices of the Convention on Biological Diversity, provides the basis for taking effective action to curb biodiversity loss across the planet by 2020-an urgent imperative. Yet, Antarctica and the Southern Ocean, which encompass 10% of the planet's surface, are excluded from assessments of progress against the Strategic Plan. The situation is a lost opportunity for biodiversity conservation globally. We provide such an assessment. Our evidence suggests, surprisingly, that for a region so remote and apparently pristine as the Antarctic, the biodiversity outlook is similar to that for the rest of the planet. Promisingly, however, much scope for remedial action exists.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.512

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 studies0.0010.001
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 head0.078
GPT teacher head0.279
Teacher spread0.201 · 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 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

Citations107
Published2017
Admission routes1
Has abstractyes

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