Ecosystem service trade-offs across global contexts and scales
Bibliographic record
Abstract
Meeting human needs while sustaining the planet's life support systems is the fundamental challenge of our time.What role sustenance of biodiversity and contrasting ecosystem services should play in achieving a sustainable future varies along philosophical, cultural, institutional, societal, and governmental divisions.Contrasting biophysical constraints and perspectives on human well-being arise both within and across countries that span the tropics and temperate zone.Direct sustenance of livelihoods from ecosystem services in East Africa contrasts with the complex and diverse relationships with the land in Mexico and the highly monetary-based economy of the United States.Lack of understanding of the contrasting contexts in which decision-making about trade-offs occurs creates impediments to collective global efforts to sustain the Earth's life support systems.While theoretical notions of the goals of sustainability science seek a unified path forward, realities on the ground present challenges.This Special Feature seeks to provide both an analytical framework and a series of case studies to illuminate impediments posed to sustainability by contrasting biophysical constraints and human perspectives on what should be sustained.The contributors aim to clarify the trade-offs posed to human welfare in sustaining biodiversity and ecosystem services and the challenges in managing for a sustainable future in which human well-being is not compromised as compared to today.Our goal is to provide novel insights on how sustainability can be achieved internationally through exploration of constraints, trade-offs, and human values examined at multiple scales, and across geographic regions from a range of cultural perspectives.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".