Bibliographic record
Abstract
Aims: Plant ex situ conservation will be a core task in the on-going construction of the national botanical garden system of China.However, many issues regarding the status and role of plant ex situ conservation in biodiversity conservation, as well as how to set up the goals for plant ex situ conservation in China, are still under discussion.The objective of this study is to address these unresolved issues. Methods:The study utilizes literature reviews and international conventions such as the "Kunming-Montreal Global Biodiversity Framework" and the upcoming "Complementary Actions Related to Plant Conservation to Support the Implementation of the Kunming-Montreal Global Biodiversity Framework".Results: Through a review of the development history and achievements of plant ex situ conservation over the past 40 years, this paper clarifies some doubts and misconceptions about plant ex situ conservation.Furthermore, this article proposes the goals of plant ex situ conservation in China, as "Establish programs to ensure ALL threatened plant species are effectively conserved by ex situ methodologies, aiming to achieve genetically diverse representations".Conclusion: The establishment of the national botanical garden system in China presents an opportunity to enhance plant ex situ conservation efforts.By improving the quality of processes involved in ex situ conservation program, China can elevate its conservation practices to a higher level.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".