Mismatches in the Ecosystem Services Literature—a Review of Spatial, Temporal, and Functional-Conceptual Mismatches
Bibliographic record
Abstract
Abstract Purpose of Review The objective of this review is to identify commonly researched ecosystem service mismatches, including mismatches concerning management and policies implemented to manage ecosystem service delivery. It additionally discusses how mismatches affect the ability to develop effective policies and management guidelines for ecosystem services. Recent Findings Recent ecosystem service literature considers mismatches in the ecosystem, the social system, and as social-ecological interactions. These mismatches occur over three dimensions: spatial, temporal, and functional-conceptual. The research field incorporates not only ecological aspects but also social ones like the management and governance of ecosystem services. However, the focus of the reviewed literature is mainly on spatial and temporal dimensions of mismatches and the production of scientific knowledge, rather than the implementation of the knowledge in management and policies. Summary Research on ecosystem service mismatches reflects the complexity and interconnectedness of social-ecological systems as it encompasses a broad variety of approaches. However, temporal mismatches received less attention than spatial mismatches, especially in regard to social and social-ecological aspects and could be a topic for future research. Furthermore, in order to develop effective policies and management guidelines, research must work closer with decision-makers to not only advance scientific understanding of ecosystem service mismatches but also create understanding and support the uptake of this knowledge.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".