The Response of the Codification of the Eco-Environmental Code to the “Mainstreaming of Biodiversity” Objective in the GBF
Bibliographic record
Abstract
The subsequent international negotiations of the Convention on Biological Diversity and the Kunming-Montreal Global Biodiversity Framework are approaching. China’s ecological civilization construction is advancing vigorously, and the draft of the Ecological Environment Code has been formulated for public consultation. Against the backdrop of these critical historical milestones, both domestic and international processes resonate with the priority area of “biodiversity mainstreaming” outlined in the National Biodiversity Strategy and Action Plan (2023-2030). The content of this priority area mainly involves two major directions: establishing a legal and regulatory system related to biodiversity, and drafting regulations on access and benefit-sharing of biological genetic resources. To meet the localized objective of “biodiversity mainstreaming,” the Ecological Environment Code, which is philosophically grounded in “harmonious coexistence between humans and nature,” must also respond to issues related to this priority area. This includes formulating rules on benefit-sharing of digital sequence information on genetic resources and establishing a “biodiversity credit market mechanism.” Such measures will not only promote the integrated development of ecological civilization construction and biodiversity conservation in China but also help shape the international discourse on China’s concept of “ecological civilization.”
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".