National mapping and assessment of ecosystem services projects in Europe – Participants’ experiences, state of the art and lessons learned
Bibliographic record
Abstract
Backed by the Biodiversity Strategy to 2020 and 2030, numerous ‘Mapping and Assessment of Ecosystem Services’ (MAES) projects have been completed in recent years in the member states of the European Union, with substantial results and insights accumulated. The experience from the different approaches is a valuable source of information for developing assessment processes further, especially with regard to their uptake into policy and more recently, into ecosystem accounting. Systematic approaches towards best practices and lessons learned from national MAES projects are yet lacking. This study presents the results of a survey conducted with participants of national MAES projects overviewing 13 European MAES processes. Focus hereby is put on the types of methods used, the assessed ecosystem services, and the perceived challenges and advancements. All MAES projects assessed ecosystem services at several levels of the ecosystem service cascade (69% at least three levels), using a diverse set of data sources and methods (with 4.7 types of methods on average). More accessible data was used more frequently (e.g., statistical and literature data being the most popular). Challenges regarding policy uptake, synthesizing results, and data gaps or reliability were perceived as the most severe. Insufficient evaluation of uncertainty was seen as a major critical point, and emphasized as crucial for uptake and implementation. Moving towards accounting for ES in the frame of environmental-economic accounts, considering uncertainties of ES assessments should be even more important.
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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 | 0.036 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".