An overview of the science–policy interface among climate change, biodiversity, and terrestrial land use for production landscapes
Bibliographic record
Abstract
Global progress in addressing climate change through mitigation and adaptation has been slow, although policy tools are available and most countries now have some climate change policies. Climate change represents a tragedy of the commons caused by all humans, but one for which the damage is slow to accumulate and cannot be readily identified as coming from a single source. As a result, politicians are slow to act. The UNFCCC (United Nations Framework Convention on Climate Change) has had minor achievements over 21 years, although the recent mitigation decision on REDD+ (reducing emissions from forest degradation and deforestation) recognizes the roles that eliminating deforestation and forest degradation and improving agriculture can play in mitigating climate change. The Cancun Agreement also states that, for mitigation to be effective, adaptation is needed. There is a strong body of literature linking biodiversity to ecosystem resilience and goods and services. Any policies dealing with mitigation and adaptation must consider the important role of biodiversity in terrestrial system recovery and management, including forests, agro-forests, and agricultural systems. In production landscapes, policies need to consider the large landscape scale and be cross-sectoral in application, including among forest, agriculture, transportation, energy, and human health sectors. Finally, local ecological knowledge and scientific information should form the basis for such policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".