National mapping and assessment of ecosystem services projects in Europe – Participants’ experiences, state of the art and lessons learned
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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