Advancing research on ecosystem service bundles for comparative assessments and synthesis
Bibliographic record
Abstract
Social-ecological interactions have been shown to generate interrelated and reoccurring sets of ecosystem services, also known as ecosystem service bundles. Given the potential utility of the bundles concept, along with the recent surge in interest it is timely to reflect on the concept, its current use and potential for the future. Based on our ecosystem service bundle experience, expertise, and ecosystem service bundle analyses, we have found critical elements for advancing the utility of ecosystem service bundle concept and deepening its impact in the future. In this paper we 1) examine the different conceptualizations of the ecosystem service bundle concept; 2) show the range of benefits of using a bundles approach; 3) explore key issues for improving research on ecosystem service bundles, including indicators, scale, and drivers and relationships between ecosystem services; and 4) outline priorities for the future by facilitating comparisons of ecosystem service bundle research.
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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 | 0.111 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.036 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".