Conservation of Biodiversity and Nature — a New Priority for Green Agenda
Bibliographic record
Abstract
The article reviews the drivers and main goals of the Kunming-Montreal Global Biodiversity Framework (GBF), adopted by 196 countries during COP-15 in December 2022. The overarching aim of the GBF is to halt and reverse nature loss by 2030, in particular restore 30 % of critical biodiversity areas. The targets should be translated into national-level policy concerning governments, global and national environment funds as well as private sector actors, including financial institutions. The GBF isanalyzed in close connection with Paris agreement on climate change 2015 and Sustainable Development Goals. It is shown that a lot of GBF’s actions follow the example of those declared in the Paris agreement. The research is focused on the evaluation of the opportunities and challenges for the realization of GBF. The largest part of its stakeholders considers biodiversity as a unifying concept, that can help reduce the current political polarization that exist in relation to decarbonization in particular and to ESG principals as a whole. But difficulties and barriers also exist. One of them is the complicity of biodiversity impact, because it is not quite clear what to measure and how to measure. Probably the largest problem here is a lack of financial resources allocated to protection of ecological systems, especially in developing countries. So, the author comes to conclusion that despite approval of the aims of the GBF, practical actions taken by governments and business are scarce, and the conservation of biodiversity is seen as priority only in public discussions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".