Developing a standardized definition of ecosystem collapse for risk assessment
Bibliographic record
Abstract
The International Union for Conservation of Nature (IUCN) Red List of Ecosystems is a powerful tool for classifying threatened ecosystems, informing ecosystem management, and assessing the risk of ecosystem collapse (that is, the endpoint of ecosystem degradation). These risk assessments require explicit definitions of ecosystem collapse, which are currently challenging to implement. To bridge the gap between theory and practice, we systematically review evidence for ecosystem collapses reported in two contrasting biomes – marine pelagic ecosystems and terrestrial forests. Most studies define states of ecosystem collapse quantitatively, but few studies adequately describe initial ecosystem states or ecological transitions leading to collapse. On the basis of our review, we offer four recommendations for defining ecosystem collapse in risk assessments: (1) qualitatively defining initial and collapsed states, (2) describing collapse and recovery transitions, (3) identifying and selecting indicators of collapse, and (4) setting quantitative collapse thresholds.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".