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Record W4381664476 · doi:10.1007/s10784-023-09608-8

Inspiration from the Kunming-Montreal Global Biodiversity Framework for SDG 15

2023· article· en· W4381664476 on OpenAlexaboutno aff
Ina Lehmann

Bibliographic record

VenueInternational Environmental Agreements Politics Law and Economics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsSummitConvention on Biological DiversityDeclarationBiodiversitySustainable developmentConventionPolitical scienceEnvironmental resource managementEarth SummitPoliticsEnvironmental planningAction planGeographyEconomicsEcologyBiologyLawCartography

Abstract

fetched live from OpenAlex

Abstract With the United Nations’ Agenda 2030, countries worldwide have committed to a set of Sustainable Development Goals (SDGs) to be achieved by 2030. Among them is SDG 15, known as Life on Land. What makes this SDG special is that several of its targets had been scheduled for completion by 2020– raising the question what should happen to these targets after 2020 as they have not yet been achieved. With the approaching 2023 SDG Summit in mind, this perspective paper examines how the Kunming-Montreal Global Biodiversity Framework, which was adopted under the Convention on Biological Diversity in late 2022, might provide guidance for the implementation of SDG 15 and maintain the momentum for action until 2030. Three areas are critical. First, concerning protected areas, the strengthened rights-based approach of the Kunming-Montreal Global Biodiversity Framework should be integrated into SDG 15. Second, the new framework promotes the sustainable use of biodiversity more clearly than SDG 15 and should hence guide transformation of the biodiversity-based economic sectors. Finally, the Kunming-Montreal Global Biodiversity Framework provides the first quantified financial target for global biodiversity action, and at the SDG Summit, that target should be reinforced by additional financial commitments. Guidance in these three areas can be integrated into the SDG Summit’s Political Declaration and into the voluntary pledges that countries are expected to make at the Summit, and it can inform the review of the SDG indicators.

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 score0.173
Threshold uncertainty score0.873

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 science0.0000.001
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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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