Goals of Kunming-Montreal Global Biodiversity Framework and solutions by China’s Biosphere Reserve Network
Bibliographic record
Abstract
Biodiversity is the core element of earth life system, and it is also the source of food, drinking water, health, security, and social welfare for people. Biodiversity contains critical genetic resources that help people to response to future uncertainties, in particular, hold solutions to climate change. However, due to population outbreak, technology advancement, and unsustainable production and consumption, the earth ecosystem has been under significant development pressure, which indicated by human ecological footprint of 170% that earth ecosystem can provide, which led to continuous loss of biodiversity. If such trend continues, the sixth massive biodiversity extinction will unavoidable. This study reviewed global biodiversity conservation theory and practices, and concludes that Kunming-Montreal Global Biodiversity Framework, had been fully taken lessons from global protected areas development, “Half Earth” proposal, global biodiversity hotspots, global eco-region planning, as well as the relevance of global inter-governmental agreements. It has been considered as the best solution to reverse global biodiversity loss at this stage. China’s Biosphere Reserve Network has played key role in delivering Kunming-Montreal Global Biodiversity Framework, including that (1) explored and implemented tools and solutions for mainstreaming and implementation of the Kunming-Montreal Global Biodiversity Framework; (2) developed harmonized development models with local communities and expanded area based other effective conservation measures; and (3) technology innovation to enable effective monitoring and managing the implementation of Kunming-Montreal 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".