A Chinese database on ecological thresholds and alternative stable states: implications for related research around the world
Bibliographic record
Abstract
The concepts of ecological thresholds and alternative stable states were proposed to explain nonlinear changes. However, the greatest obstacle to advance these theories and their managerial applications is a lack of data and research experience. There are almost all types of ecosystems in China, and various ecological degradation and catastrophe events occurred at the end of the 20th century. Considerable monitoring data and research cases that focus on the ecological thresholds are published in Chinese, limiting their dissemination around the world. We integrate Chinese cases and data that refer to the framework of Threshold Database and Regime Shifts Database. We introduce the China Ecological Thresholds and Alternative Stable States Database (CETASSD), developed by the Chinese Research Academy of Environmental Sciences, which mainly collects research cases. The CETASSD uses a unified description framework to integrate key information from past 110 case studies from China. This paper summarizes relevant case studies with intrinsic consistency to ecological thresholds and alternative stable states in social-ecological systems. We collate and analyze 26 potential alternative stable states and 60 potential ecological thresholds in CETASSD, covering 14 types of ecosystems. We found several peculiarities of the Chinese case studies. First, more types of alternative stable states were identified in arid areas and Qinghai-Tibet Plateau. Second, critical thresholds research related to spatial gradient has received great attention. Third, methods of constructing highly generalized “stress-response” process lines are mainly used for threshold analysis. We suggest re-examining past research cases and methods with the latest theories of ecological thresholds and alternative stable states; strengthening research on the detection of threshold and mechanism establishment of certain ecosystems, such as the ocean in China; and further applying ecological thresholds to ecological assessment and early warning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".