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Record W3027315263 · doi:10.1016/j.oneear.2020.04.013

To Achieve Big Wins for Terrestrial Conservation, Prioritize Protection of Ecoregions Closest to Meeting Targets

2020· article· en· W3027315263 on OpenAlex

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

VenueOne Earth · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Northern British Columbia
FundersAustralian Research CouncilUniversity of QueenslandCentre of Excellence for Environmental Decisions, Australian Research Council
KeywordsEcoregionFutures contractHabitatEnvironmental resource managementTerrestrial ecosystemGeographyPlan (archaeology)EcosystemEnvironmental protectionAgroforestryEnvironmental scienceBusinessEcologyBiology

Abstract

fetched live from OpenAlex

Most of the terrestrial world is experiencing high rates of land conversion despite growth of the global protected area (PA) network. There is a need to assess whether the current global protection targets are achievable across all major ecosystem types and to identify those that need urgent protection. Using recent rates of habitat conversion and protection and the latest terrestrial ecoregion map, we show that if the same approach to PA establishment that has been undertaken over the past three decades continues, 558 of 748 ecoregions (ca. 75%) will not meet an aspirational 30% area protection target by 2030. A simple yet strategic acquisition plan that considers realistic futures around habitat loss and PA expansion could more than double the number of ecoregions adequately protected by 2030 given current funding constraints. These results highlight the importance of including explicit ecoregional representation targets within any new post-2020 global PA target.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.348

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)0.0000.000

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 head0.054
GPT teacher head0.218
Teacher spread0.164 · 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