Realitäts-Check auf regionaler Ebene: Implikationen der CBD-COP15 für Sachsen
Bibliographic record
Abstract
Reaching the goals of the Kunming-Montreal Global Biodiversity Framework requires commitment at all political levels and in all sectors. The State of Saxony also has to contribute its share. Saxony has a great potential, but also faces particular challenges. Almost half of the land area is used for agriculture, mainly for arable farming. However, only around eight percent of the land is farmed ecologically4. Intensification and monotonization of agriculture, as well as the use of pesticides and fertilizers, significantly contribute to the loss of biodiversity. Agriculture plays a crucial role as a habitat for biodiversity5. It is indispensable to promote biodiversity-friendly use, increase the proportion of land under organic farming, and establish corresponding methods in conventional agriculture. As a producer of fossil fuels, especially by the Lusatian and Central German lignite mining regions, Saxony is also strongly affected by the energy transition. The expansion of renewable energies needs to be nature-compatible and in harmony with the protection of biodiversity. Approaches to multifunctional landuse may provide support in this regard. Prof. Dr. Edeltraud Günther, Director of UNU-FLORES, emphasizes the need to consider biodiversity in the resource nexus. Saxony has good prerequisites to meet these challenges. With its Saxony Biodiversity 2030 Program, it has a revised biodiversity strategy to meet the global targets. In addition, Saxony is home to major research institutions that intensively focus on biodiversity. Research, education, and science communication play a central role in this context. Prof. Tshilidzi Marwala, Rector of UNU and Under-Secretary- General of the UN, emphasized the key role of education in his opening address of the DNCi 2023: 'Education is the key to unlock our potential. It empowers us to become stewards of our environment by providing us with a deep appreciation for biodiversity and inspiring sustainable practices in every aspect of our lives. By integrating transformative education at the international, national, and local levels, we can create profound change in attitudes, knowledge, and behaviors.' The DNCi 2023 participants had a hands-on experience of the importance of education and science communication on biodiversity thanks to a guided tour of the Botanical Garden. Many thanks to Prof. Dr. Christoph Neinhuis, Director of the Botanical Garden, and Dr. Barbara Dietsch, Scientific Director of the Botanical Garden, for these valuable insights. As part of the DNCi 2023, co-organized by UNU-FLORES, the IOER, and TU Dresden, we succeeded in bringing together different stakeholders from science, government, civil society, and the private sector to create a dynamic platform for exchange and collaboration on the topic of biodiversity. We would like to express our sincere gratitude to all participants for their commitment during the event and beyond, and to the Saxon State Ministry of Energy, Climate Protection, Environment and Agriculture for supporting the event within the framework of its cooperation with UNU-FLORES.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.006 |
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".