An overview of the science–policy interface among climate change, biodiversity, and terrestrial land use for production landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it