Inferring Models with Alternative Stable States from Independent Observations
Bibliographic record
Abstract
Abstract Multiple attractors and alternative stable states are defining features of scientific theories in ecology and evolution, implying that abrupt regime shifts can occur and that outcomes can be hard to reverse. Here we describe a statistical inferential framework that uses independent, noisy observations with low temporal resolution to support or refute multiple attractor process models. The key is using initial conditions to choose among a finite number of expected outcomes using a nonstandard finite mixture methodology. We apply the framework to contemporary issues in social-ecological systems, coral ecosystems, and chaotic systems, showing that incorporating history allows us to statistically infer process models with alternative stable states while minimizing false positives. Further, in the presence of disturbances and oscillations, alternative stable states can help rather than hamper inference. The ability to infer models with alternative stable states across natural systems can help accelerate scientific discoveries, change how we manage ecosystems and societies, and place modern theories on firmer empirical ground.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".