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Record W2933098123 · doi:10.1111/conl.12646

Restoration priorities to achieve the global protected area target

2019· article· en· W2933098123 on OpenAlex
Bonnie Mappin, Aliénor L. M. Chauvenet, Vanessa M. Adams, Moreno Di Marco, Hawthorne L. Beyer, Oscar Venter, Benjamin S. Halpern, Hugh P. Possingham, James Watson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConservation Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Northern British Columbia
FundersHorizon 2020 Framework ProgrammeUniversity of Queensland
KeywordsProtected areaEnvironmental resource managementAgricultureRestoration ecologyLand degradationEnvironmental scienceGeographyAgroforestryEnvironmental protectionEnvironmental planningNatural resource economicsEcologyBiology

Abstract

fetched live from OpenAlex

Abstract With much of Earth's surface already heavily impacted by humans, there is a need to understand where restoration is required to achieve global conservation goals. Here, we show that at least 1.9 million km 2 of land, spanning 190 (27%) terrestrial ecoregions and 114 countries, needs restoration to achieve the current 17% global protected area target (Aichi Target 11). Restoration targeted on lightly modified land could recover up to two‐thirds of the shortfall, which would have an opportunity cost impact on agriculture of at least $205 million per annum (average of $159/km 2 ). However, 64 (9%) ecoregions, located predominately in Southeast Asia, will require the challenging task of restoring areas that are already heavily modified. These results highlight the need for global conservation strategies to recognize the current level of anthropogenic degradation across many ecoregions and balance bigger protected area targets with more specific restoration goals.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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