Scaling up wetland conservation and restoration to deliver the Kunming-Montreal Global Biodiversity Framework: Guidance on including wetlands in National Biodiversity Strategy and Action Plans (NBSAPs) to boost biodiversity and halt wetland loss and degradation
Bibliographic record
Abstract
Wetland conservation and restoration is an essential component of delivering the Kunming-Montreal Global Biodiversity Framework (KM-GBF) vision of a world living in harmony with nature where “by 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people.” This document supports the inclusion of ambitious wetland commitments and actions in National Biodiversity Strategies and Action Plans (NBSAPs) as a pivotal way of boosting biodiversity, to achieve the goals of both the Convention on Wetlands and the KM-GBF. This report focuses on the critical role of wetlands in achieving the 23 targets of the KM-GBF by 2030. It provides guidance to Parties to the CBD on how to incorporate the role and importance of wetlands and key actions into their NBSAPs in relation to each target. It also provides Contracting Parties to the Convention on Wetlands, with information on how to support delivery of the KM-GBF and achieve targets within the Fourth Strategic Plan of the Convention on Wetlands and the forthcoming Fifth Strategic Plan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".