Bibliographic record
Abstract
在生物多样性公约第十五次缔约方大会通过《昆明-蒙特利尔全球生物多样性框架》的背景下，阐述深圳市生物多样性现状，识别出辖区在生物多样性保护方面存在的一些问题，分别是评估体系不完善、森林质量不高、项目建设和人为活动影响较大、外来入侵物种对本土物种存在威胁。针对这些问题提出了深圳市生物多样性保护的路径，包括构建区域生物多样性评估体系和提升辖区生物多样性治理水平。以期为深圳市及我国其他超大城市的生物多样性保护工作提供参考。 In light of the adoption of the Kunming-Montreal Global Biodiversity Framework during the 15th Conference of the Parties to the Convention on Biological Diversity, this paper examines the current conditions of biodiversity and identifies several challenges facing biodiversity conservation in Shenzhen. These challenges include an inadequate evaluation system, low forest quality, significant impacts from construction projects and human activities, as well as threats posed by invasive alien species to native flora and fauna. To address these issues, this study proposes a comprehensive strategy for enhancing biodiversity conservation in Shenzhen that encompasses establishing a robust biodiversity evaluation framework and improving biodiversity governance within the jurisdiction. This study aims to provide a reference for similar conservation efforts in Shenzhen and other megacities across China.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".