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Record W4403470867 · doi:10.2305/laaw4624

Guidance on other effective area-based conservation measures (OECMs)

2024· book· en· W4403470867 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsnot available
FundersBundesamt für Naturschutz
KeywordsComputer science

Abstract

fetched live from OpenAlex

The Kunming-Montreal Global Biodiversity Framework, adopted in 2022, provides a framework for the effective implementation of the Convention on Biological Diversity (CBD) through four goals and 23 targets. Target 3 (known as the ‘30x30 target’) calls on Parties to conserve at least 30% of terrestrial, inland waters, and coastal and marine areas by 2030. Target 3 is incentivising government agencies and other actors to advance diverse kinds of inclusive, equitable and effective area-based conservation. These guidelines are designed to promote good practices relating to identifying, reporting, monitoring and strengthening OECMs. They are intended for use by a wide range of rightsholders and stakeholders to promote understanding of whether a site meets the CBD criteria for identifying an OECM, how to report OECM data at the national and global levels, and how to monitor and strengthen OECMs.

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)0.008
metaresearch head score (Gemma)0.016
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 score0.086
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0060.003
Research integrity0.0130.006
Insufficient payload (model declined to judge)0.0860.050

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 head0.017
GPT teacher head0.222
Teacher spread0.204 · 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

Citations18
Published2024
Admission routes1
Has abstractyes

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