Managing Protected Areas in Multi-functional Landscapes - Supporting the implementation of the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Human activities have caused biodiversity to decline at an alarming rate in recentdecades, with land and sea use changes, overexploitation of natural resources,pollution, invasive alien species and climate change as the main drivers (IPBES, 2019).The resulting numbers speak for themselves: 75% of the Earth’s surface has beensignificantly altered, global wildlife populations have declined by 69% in the last fiftyyears, and pollution is threatening all ecosystems, for instance, 90% of ocean speciesthat were assessed are adversely affected by plastic pollution (Tekman et al., 2022;WWF, 2022).To address the triple planetary crisis, and to put nature on a path to recovery,governments adopted the Kunming-Montreal Global Biodiversity Framework (GBF) inDecember 2022. Amongst the several provisions of the document, Target 3 calls forthe protection and effective conservation of at least 30% of the planet by 2030, whileTarget 1 calls for all areas to be under participatory, integrated and biodiversity inclusivespatial planning and/or effective management processes (CBD, 2022).Protected areas have become the cornerstone for biodiversity conservation worldwide.They need to protect key habitats and species, and simultaneously support naturalprocesses across various landscapes. However, with humans present and invested inmost land- and seascapes, protected areas need to be compatible with many differentcircumstances and surroundings (Hughes & Grumbine, 2023; IPBES, 2019). Here iswhere RECONNECT can contribute with key insights and support the implementationof the Global Biodiversity Framework.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".