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Record W3085078608 · doi:10.1038/s41467-020-18457-x

Just ten percent of the global terrestrial protected area network is structurally connected via intact land

2020· article· en· W3085078608 on OpenAlexaff
Michelle Ward, Santiago Saura, Brooke Williams, Juan Pablo Ramírez‐Delgado, Nur Arafeh‐Dalmau, James R. Allan, Oscar Venter, Grégoire Dubois, James Watson

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Northern British Columbia
FundersJoint Research CentreUniversity of QueenslandEuropean CommissionAustralian GovernmentNational Aeronautics and Space Administration
KeywordsBiodiversityContext (archaeology)Set-asideEnvironmental resource managementHabitatDisturbance (geology)EstateProtected areaAgroforestryGeographyBusinessEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Land free of direct anthropogenic disturbance is considered essential for achieving biodiversity conservation outcomes but is rapidly eroding. In response, many nations are increasing their protected area (PA) estates, but little consideration is given to the context of the surrounding landscape. This is despite the fact that structural connectivity between PAs is critical in a changing climate and mandated by international conservation targets. Using a high-resolution assessment of human pressure, we show that while ~40% of the terrestrial planet is intact, only 9.7% of Earth's terrestrial protected network can be considered structurally connected. On average, 11% of each country or territory's PA estate can be considered connected. As the global community commits to bolder action on abating biodiversity loss, placement of future PAs will be critical, as will an increased focus on landscape-scale habitat retention and restoration efforts to ensure those important areas set aside for conservation outcomes will remain (or become) connected.

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 score0.044
Threshold uncertainty score0.385

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations231
Published2020
Admission routes1
Has abstractyes

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