Using Landscape Approaches in National Biodiversity Strategy and Action Planning
Bibliographic record
Abstract
This publication explores the transformative potential of landscape approaches in biodiversity conservation, advocating for holistic strategies that reconcile diverse landscape and seascape uses. Emphasizing direct and indirect applications, it highlights the pivotal role of national governments, subnational authorities, indigenous communities, and private landowners. By fostering collaboration and establishing shared visions, stakeholders can create sustainable management plans, outlined within National Biodiversity Strategies and Action Plans (NBSAPs). The publication delves into the integration of landscape approaches into national conservation targets, as exemplified by the Kunming-Montreal Global Biodiversity Framework. It underscores the importance of proactive engagement, emphasizing meticulous monitoring and documentation of successes and failures, shared through national reports. Furthermore, the publication explores cross-sector plans and sector-specific strategies as effective channels for integrating landscape approaches, aligning conservation with diverse land and sea use activities. In essence, this guide champions a unified, adaptable, and inclusive approach, offering a roadmap towards harmonizing human activities with the preservation of biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".