Balancing the books of nature by accounting for ecosystem condition following ecological restoration
Bibliographic record
Abstract
Demand for ecological restoration of Earth's degraded ecosystems has increased significantly since the adoption of The Kunming-Montreal Global Biodiversity Framework in December 2022, with target 2 aiming to ensure that at least 30% of degraded ecosystems are under effective restoration by 2030. More recently, in December 2023, the Australian Parliament introduced the Nature Repair Act, which establishes a framework for the world's first legislated, national, voluntary biodiversity market. How can the effectiveness of these ambitious targets be measured? Natural Capital Accounting (NCA) provides a framework to measure changes in ecosystem condition that is applicable across ecosystems and potentially catalogue effects of restoration interventions to drive investment, improvement to practice, and ultimately, to better protect the Earth's ecosystems. However, the framework has not been tested in this context. In this progressive approach, we populated the leading global NCA framework with ecological data to quantify changes in ecosystem condition after restoration. In principle, NCA is fit for purpose, however, methodological refinements and ecological expertise are needed to unlock its full potential. These tweaks will facilitate adoption and standardisation of reporting as efforts ramp up to meet ambitious global restoration targets.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".