Can we detect ecosystem critical transitions and signals of changing resilience from paleo‐ecological records?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Nonlinear responses to changing external pressures are increasingly studied in real‐world ecosystems. However, as many of the changes observed by ecologists extend beyond the monitoring record, the occurrence of critical transitions, where the system is pushed from one equilibrium state to another, remains difficult to detect. Paleo‐ecological records thus represent a unique opportunity to expand our temporal perspective to consider regime shifts and critical transitions, and whether such events are the exception rather than the rule. Yet, sediment core records can be affected by their own biases, such as sediment mixing or compression, with unknown consequences for the statistics commonly used to assess regime shifts, resilience, or critical transitions. To address this shortcoming, we developed a protocol to simulate paleolimnological records undergoing regime shifts or critical transitions to alternate states and tested, using both simulated and real core records, how mixing and compression affected our ability to detect past abrupt shifts. The smoothing that is built into paleolimnological data sets apparently interfered with the signal of rolling window indicators, especially autocorrelation. We thus turned to time‐varying autoregressions (online dynamic linear models, DLM s; and time‐varying autoregressive state‐space models, TVARSS ) to evaluate the possibility of detecting regime shifts and critical transitions in simulated and real core records. For the real cores, we examined both varved (annually laminated sediments) and non‐varved cores, as the former have limited mixing issues. Our results show that state‐space models can be used to detect regime shifts and critical transitions in some paleolimnological data, especially when the signal‐to‐noise ratio is strong. However, if the records are noisy, the online DLM and TVARSS have limitations for detecting critical transitions in sediment records.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it