A systematic conservation planning approach to maintaining ecosystem service provision in working landscapes
Bibliographic record
Abstract
Balancing human well-being with the maintenance of ecosystem services (ES) for future generations has become one of the central sustainability challenges of the 21st century. In working landscapes, past and ongoing production-centered objectives have resulted in the conversion of ecosystems into simple land-use types, which has also altered the provision of most ES. These inevitable trade-offs between the efficient production of individual provisioning ES and the maintenance of regulating and cultural ES call for the development of a land-use strategy based on the multifunctional use of the landscape. Due to the heterogeneous nature of working landscapes, both protection and restoration actions are needed to improve their multifunctionality. Systematic conservation planning (SCP) offers a decision support framework that can support landscape multifunctionality by indicating where ES management efforts should be implemented. We describe an approach that we developed to include ES provision protection and restoration objectives in SCP with the goal of providing ongoing benefits to society. We explain the general framework of this approach and discuss concepts, challenges, innovations, and prospects for the further development of a comprehensive decision support tool. We illustrate our approach with two case studies implemented in the pan-Canadian project ResNet.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| 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".