Spatiotemporal Changes and Trade-Offs/Synergies of Waterfront Ecosystem Services Globally
Bibliographic record
Abstract
The types of ecosystem services are complex and diverse. This study estimated four ecosystem services, their trade-offs, and their synergistic outcomes in 2010, 2015, and 2020 using the InVEST model. Globally, results showed that waterfront ecosystem services are high (low) in the north (south), and that high-value areas are mainly in Sweden and Finland in Europe; the Congo in Africa; Russia, Southwest China, and Indonesia in Asia; the Northwestern United States and Canada in North America, and northeastern Peru and northwestern Brazil in South America. Overall, ecosystem services changed little during 2010–2020. Additionally, a notable trade-off was found between water yield and habitat quality, and strong synergies were found between water yield and carbon storage and sequestration, water yield and sediment delivery ratio, carbon storage and sequestration and sediment delivery ratio, and carbon storage and habitat quality. The results of this study could help promote sustainable development of waterfronts globally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".