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Record W4390699273 · doi:10.3389/fcosc.2023.1324928

The contribution of private land conservation to 30x30 in Germany

2024· article· en· W4390699273 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Conservation Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersBundesamt für Naturschutz
KeywordsEasementGermanBusinessEuropean unionEnvironmental planningGeographyNature ConservationEnvironmental resource managementEnvironmental protectionPolitical scienceEconomicsInternational tradeEcologyLaw

Abstract

fetched live from OpenAlex

In line with Target 3 of the Kunming-Montreal Global Biodiversity Framework, the European Union (EU) aims to protect 30% of its land and sea by 2030 (known as 30x30). Germany has been a vocal supporter of this goal in the international arena but has yet to achieve sufficient protected area coverage domestically. We estimate that Germany needs to report an additional 4.65 million hectares of protected land to achieve 30x30. This article examines the potential of privately protected areas (PPAs) and other effective area-based conservation measures (OECMs) to contribute to this goal. We explore the German Federal Nature Conservation Act and identify the legal hurdles for the designation and recognition of PPAs. Furthermore, we argue that OECMs have the potential to contribute significantly to 30x30 in Germany. We estimate that close to one million hectares of land could be classified as OECMs and outline potentially qualifying sites. In conclusion, we discuss the prerequisites for upscaling private land conservation in Germany, focusing on required conditions for establishing OECMs and incentivising conservation easements and long-term conservation leases through national funding programmes.

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)0.002
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 score0.113
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.227
Teacher spread0.219 · 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