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Record W7139246248

Protected Planet Report 2024

2024· book· en· W7139246248 on OpenAlexaboutno aff
Emily Howland, Heather Bingham, Kelly Malsch, Matt Kaplan, Neil David Burgess, Marine Deguignet, Thierry Lefebvre, James Hardcastle, Stephen Woodley, Nigel Dudley, Madhu Rao

Bibliographic record

VenueResearch at the University of Copenhagen (University of Copenhagen) · 2024
Typebook
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Context (archaeology)BiodiversityPlanetGlobal biodiversityGlobal networkProtected area
DOInot available

Abstract

fetched live from OpenAlex

The Protected Planet Report 2024 is the first report to fully assess the global status of protected and conserved areas in the context of Target 3 of the Kunming-Montreal Global Biodiversity Framework. The report brings together the latest official data reported by governments and other stakeholders to the Protected Planet Initiative. The aim of Target 3 is to expand the global network of protected and conserved areas to 30% coverage in a way that is equitable and that respects the rights of Indigenous Peoples and local communities. The aim is also to ensure that these areas are effective, well-connected and strategically located in the places that are most important for biodiversity and ecosystem services. Each chapter in the report is dedicated to a separate element of Target 3. In this way, the document assesses progress not just towards 30% coverage but also the full scope of other important elements of the target, including towards improving the quality of protected and conserved areas around the world.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.111
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1110.105

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 head0.032
GPT teacher head0.231
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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