Ecosystem services in urban forest areas: balancing carbon storage and biodiversity
Bibliographic record
Abstract
Context and matrix have received increasing importance in understanding and empowering local socioecological sustainability in \nrural communities. The ecosystem services approach, in particular, has provided innovative tools and possibilities to weigh, \ncompare, and balance various services and goods that are made available in different habitats and land cover types in a landscape \ncontinuum. This study includes large-landscape case studies in Sweden, Canada, and Chile (the Vilhelmina, Prince Albert, and \nAraucarias del Alto Malleco Model Forests). Despite different premises, the case studies share fundamental aspects of rural \ncommunity sustainability problems and solutions, e.g., marginalized indigenous peoples, dependence of natural resources, and the \nimportance of small-scale and site-specifi c livelihood and manufacturing of natural resources. In addition, the study sites represent \na comprehensive gradient of duration and degree of land-use impact and, hence, the need for landscape restoration. In an \necosystem services context these case studies allow for multifunctional, scale-independent and spatially explicit assessments of \ngood and services from alpine, agricultural, and forest habitats. Opportunities and barriers for sustainability, from various \nperspectives, are explored using large-landscape modeling, planning, and scenario analyses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".