Linked indicator sets for addressing biodiversity loss
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
Abstract The target adopted by world leaders of significantly reducing the rate of biodiversity loss by 2010 was not met but this stimulated a new suite of biodiversity targets for 2020 adopted by the Parties to the Convention on Biological Diversity (CBD) in October 2010. Indicators will be essential for monitoring progress towards these targets and the CBD will be defining a suite of relevant indicators, building on those developed for the 2010 target. Here we argue that explicitly linked sets of indicators offer a more useful framework than do individual indicators because the former are easier to understand, communicate and interpret to guide policy. A Response-Pressure-State-Benefit framework for structuring and linking indicators facilitates an understanding of the relationships between policy actions, anthropogenic threats, the status of biodiversity and the benefits that people derive from it. Such an approach is appropriate at global, regional, national and local scales but for many systems it is easier to demonstrate causal linkages and use them to aid decision making at national and local scales. We outline examples of linked indicator sets for humid tropical forests and marine fisheries as illustrations of the concept and conclude that much work remains to be done in developing both the indicators and the causal links between them.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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