The role of natural forests, including primary forests and intact forest landscapes, in climate mitigation and limiting global warming to the Paris Agreement target of 1.5 °C
Bibliographic record
Abstract
This Science information Policy Briefing Note has been prepared for the United Nations Climate COP 28. This Briefing Note provides information on the contribution of natural forests, including primary forests and intact forest landscapes, to climate mitigation and meeting the Paris Agreement’s long term temperature goal and intermediate targets as guided by science. Protecting primary forests, including intact forest landscapes, and ecologically restoring degraded natural forests, is an essential mitigation action that needs to be implemented in parallel with achieving deep and rapid cuts in fossil fuel emissions. The Kunming-Montreal Global Biodiversity Framework Target 3 aims by 2030 for at least 30% of areas to be conserved through protected areas and Other Effective Area-based Conservation Measures (OECMs). In addition to their biodiversity value, natural forests, especially primary forests, because of their natural carbon sequestration and storage capacity, can make significant and irreplaceable contributions to climate mitigation and warrant being prioritised.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.022 |
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".