Thresholds in Ecological and Social–Ecological Systems: a Developing Database
Bibliographic record
Abstract
Increasing interest in regime shifts in ecological and linked social-ecological systems (SESs) has placed a strong focus on the thresholds of change. However, research into this topic has been hampered by a lack of empirical data. This paper describes a developing database established to address this need. The database is freely available and comprises a set of summarized published examples and a searchable bibliographic database of publications on the topic. Thresholds in the database are characterized in terms of a standardized set of 24 descriptors, including the variables along which they occur, the variables that change, and the factors that have driven the change. Readers are encouraged to contribute new examples. Examples range from conceptual models to empirical evidence. The former predominate in the literature and, although they make valuable contributions and will continue to be included, the intention is build up the number of examples based on data. Examples are presented in terms of whether the threshold occurs in the ecological system, the social system, or both, and the direction of interactions between systems. The paper concludes with some initial observations on thresholds based on the examples included so far, and poses some questions for future research. Research on a typology of thresholds is a priority topic in the emerging area of "sustainability science" and it requires a rich database of empirical data.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.032 | 0.060 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".