Identifying future research directions for biodiversity, ecosystem services and sustainability: perspectives from early-career researchers
Bibliographic record
Abstract
We aimed to identify priority research questions in the field of biodiversity, ecosystem services and sustainability (BESS), based on a workshop held during the NRG BESS Conference for Early Career Researchers on BESS, and to compare these to existing horizon scanning exercises. This work highlights the need for improved data availability through collaboration and knowledge exchange, which, in turn, can support the integrated valuation and sustainable management of ecosystems in response to global change. In addition, clear connectivity among different research themes in this field further emphasizes the need to consider a wider range of topics simultaneously to ensure the sustainable management of ecosystems for human wellbeing. In contrast to other horizon scanning exercises, our focus was more interdisciplinary and more concerned with the limits of sustainability and dynamic relationships between social and ecological systems. The identified questions could provide a framework for researchers, policy makers, funding agencies and the private sector to advance knowledge in biodiversity and ES research and to develop and implement policies to enable sustainable future development.
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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 | 0.076 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".