International Case Studies of Ecosystem Accounting
Bibliographic record
Abstract
The world is increasingly recognising the global crisis in biodiversity and building a response. Biodiversity in Ireland is no less in crisis: The National Biodiversity Action Plan 2023-2030 set out the problem as: ?Despite ongoing conservation and restoration efforts, Ireland?s biodiversity is in a state of crisis, and urgent, impactful action is imperative to prevent the continued erosion of our natural heritage? (DHLGH, 2022: 6). As part of building a response to this crisis, a number of countries have developed and built a statistical system to recognise the contributions of nature to the economy and society, natural capital accounting. \n \nThis paper will provide an overview of international experience in natural capital and, in particular, ecosystem accounting and present the policy and legal context for these tools in the UN and the EU. This paper will then focus on examining implementation in the UK, Netherlands, and Mexico in some detail and a brief introduction to relevant developments in Australia, Canada and the U.S.A.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".