Effectiveness of Heilongjiang Nanwenghe Nature Reserve in Improving Habitat Quality in and around the Reserve
Bibliographic record
Abstract
Abstract Biodiversity loss is a critical challenge globally, and protected areas (PAs) has been established as an important policy tool for conservation. However, doubts exist regarding their effectiveness, and their policy effects and spatial spillover effects on surrounding areas are poorly understood. To address this, this study evaluated the effectiveness of Heilongjiang Nanwenghe National Nature Reserve (HNNNR) in China by using a combination of the InVEST model and the improved SDID model. The study covers a time span of approximately 31 years (1990–2020) and is divided into two periods (1990–1999 and 1999–2020), which allows for the assessment of the effects of nature reserves in the region. Our results showed that: (1) The establishment of HNNNR has improved the habitat quality in the reserve and Non-reserve area, with a greater impact on habitat quality in non-reserve areas than in the reserve; (2) The core zone within HNNNR showed the most significant improvement in habitat quality, while the buffer zone showed the least improvement; (3) The improvement of habitat quality in non-reserve area was mainly contributed by the policy spatial spillover effects, where the buffer zone has the strongest spillover benefits to the non-reserve, but the core zone has the weakest spillover effects to the non-reserve. Our results show the beneficial impact of a nature reserve for improving habitat quality in and around the reserve. This study provides a quantitative paradigm for assessing the conservation effectiveness of PAs across temporal and spatial scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".