Identification of priority areas for ecological restoration based on ecological security patterns and ecological risks: A case study of the Hefei Metropolitan Area
Bibliographic record
Abstract
Intensifying human activities have triggered significant ecological degradation, necessitating innovative approaches to ecosystem restoration. This study introduces a novel integrated methodology combining Ecological Security Patterns (ESP) and Ecological Risk Assessment (ERA) to identify priority ecological restoration areas in the Hefei Metropolitan Area. By synthesizing these complementary approaches, we overcome the limitations of individual methods and establish a comprehensive framework for prioritizing ecological restoration. We construct a complex ecological network comprising 36 source areas spanning 8313.96 km 2 and 92 interconnected ecological corridors extending 24,489.17 km. We have identified 73 ecological restoration nodes and 19 key restoration areas covering 544.45 km 2 , predominantly located at critical ecological junctions. The study categorizes restoration zones into five distinct types: river and lake wetland restoration, mine environment remediation, urban ecological landscape reconstruction, ecological corridor connectivity restoration, and soil and water conservation improvement. Combining ESP with ERA allows for the identification of regions most vulnerable to ecological damage while preserving key ecological functions and networks. Through the identification of urban ecological conflict zones, this study provides a strategic framework for enhancing ecosystem resilience and promoting sustainable urban development. This research is significant because of its potential to address the urgent need for effective ecological restoration strategies in rapidly urbanizing regions, offering a systematic approach to balance ecological preservation with urban development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".